from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm
from reportlab.lib.colors import HexColor, white, black
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, KeepTogether
)
from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_JUSTIFY
from reportlab.platypus import PageBreak

OUTPUT = "/home/daytona/workspace/de-novo-drug-design/De_Novo_Drug_Design_Notes.pdf"

# ── colour palette ──────────────────────────────────────────────────────────
DARK_BLUE   = HexColor("#1A3A5C")
MID_BLUE    = HexColor("#2E6DA4")
LIGHT_BLUE  = HexColor("#D6E8F7")
ACCENT_TEAL = HexColor("#1E8C8C")
ACCENT_GOLD = HexColor("#D4820A")
LIGHT_GREY  = HexColor("#F4F6F9")
MID_GREY    = HexColor("#BFC9D4")
DARK_GREY   = HexColor("#3D4A56")
RED_BOX     = HexColor("#FFF0F0")
RED_BORDER  = HexColor("#C0392B")
GREEN_BOX   = HexColor("#F0FFF4")
GREEN_BORDER= HexColor("#27AE60")

def build_pdf():
    doc = SimpleDocTemplate(
        OUTPUT,
        pagesize=A4,
        leftMargin=2*cm, rightMargin=2*cm,
        topMargin=2.2*cm, bottomMargin=2.2*cm,
        title="De Novo Drug Design – Study Notes",
        author="Orris Medical AI"
    )

    styles = getSampleStyleSheet()

    # ── custom styles ────────────────────────────────────────────────────────
    def S(name, **kw):
        return ParagraphStyle(name, **kw)

    title_style = S("DocTitle",
        fontName="Helvetica-Bold", fontSize=24, leading=30,
        textColor=white, alignment=TA_CENTER, spaceAfter=4)

    subtitle_style = S("DocSubtitle",
        fontName="Helvetica", fontSize=12, leading=16,
        textColor=HexColor("#B8D4F0"), alignment=TA_CENTER, spaceAfter=2)

    date_style = S("DocDate",
        fontName="Helvetica-Oblique", fontSize=9,
        textColor=HexColor("#90B8D8"), alignment=TA_CENTER)

    h1_style = S("H1",
        fontName="Helvetica-Bold", fontSize=15, leading=19,
        textColor=white, spaceBefore=14, spaceAfter=6,
        backColor=DARK_BLUE, leftIndent=-6, rightIndent=-6,
        borderPad=6)

    h2_style = S("H2",
        fontName="Helvetica-Bold", fontSize=12, leading=15,
        textColor=DARK_BLUE, spaceBefore=10, spaceAfter=4,
        borderPadding=(0,0,2,0))

    h3_style = S("H3",
        fontName="Helvetica-BoldOblique", fontSize=10.5, leading=13,
        textColor=ACCENT_TEAL, spaceBefore=8, spaceAfter=3)

    body_style = S("Body",
        fontName="Helvetica", fontSize=9.5, leading=14,
        textColor=DARK_GREY, spaceAfter=5, alignment=TA_JUSTIFY)

    bullet_style = S("Bullet",
        fontName="Helvetica", fontSize=9.5, leading=14,
        textColor=DARK_GREY, spaceAfter=3,
        leftIndent=14, bulletIndent=0,
        bulletFontName="Helvetica", bulletFontSize=9.5)

    sub_bullet_style = S("SubBullet",
        fontName="Helvetica", fontSize=9, leading=13,
        textColor=DARK_GREY, spaceAfter=2,
        leftIndent=28, bulletIndent=14)

    caption_style = S("Caption",
        fontName="Helvetica-Oblique", fontSize=8, leading=11,
        textColor=HexColor("#607080"), alignment=TA_CENTER, spaceAfter=4)

    ref_style = S("Ref",
        fontName="Helvetica-Oblique", fontSize=8, leading=11,
        textColor=HexColor("#607080"), spaceAfter=2, leftIndent=10)

    label_style = S("Label",
        fontName="Helvetica-Bold", fontSize=8.5,
        textColor=ACCENT_GOLD)

    # ── helper: section header with coloured bar ─────────────────────────────
    def section_header(text, level=1):
        if level == 1:
            return [
                Spacer(1, 0.15*cm),
                Table([[Paragraph(text, h1_style)]],
                      colWidths=[17*cm],
                      style=TableStyle([
                          ("BACKGROUND", (0,0), (-1,-1), DARK_BLUE),
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                          ("RIGHTPADDING", (0,0), (-1,-1), 10),
                          ("TOPPADDING",   (0,0), (-1,-1), 6),
                          ("BOTTOMPADDING",(0,0), (-1,-1), 6),
                          ("ROUNDEDCORNERS", [4]),
                      ])),
                Spacer(1, 0.2*cm),
            ]
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            return [
                Spacer(1, 0.1*cm),
                HRFlowable(width="100%", thickness=2, color=MID_BLUE, spaceAfter=3),
                Paragraph(text, h2_style),
            ]
        else:
            return [Paragraph(text, h3_style)]

    def bullet(text, sub=False):
        st = sub_bullet_style if sub else bullet_style
        symbol = "◦" if sub else "•"
        return Paragraph(f"{symbol}  {text}", st)

    def coloured_box(text, bg=LIGHT_BLUE, border=MID_BLUE, label=""):
        content = f"<b>{label}</b>  {text}" if label else text
        return Table(
            [[Paragraph(content, body_style)]],
            colWidths=[17*cm],
            style=TableStyle([
                ("BACKGROUND",    (0,0), (-1,-1), bg),
                ("BOX",           (0,0), (-1,-1), 1.5, border),
                ("LEFTPADDING",   (0,0), (-1,-1), 10),
                ("RIGHTPADDING",  (0,0), (-1,-1), 10),
                ("TOPPADDING",    (0,0), (-1,-1), 7),
                ("BOTTOMPADDING", (0,0), (-1,-1), 7),
            ])
        )

    def two_col_table(rows, headers=None):
        col_w = [5.5*cm, 11*cm]
        data = []
        if headers:
            data.append([
                Paragraph(f"<b>{headers[0]}</b>", body_style),
                Paragraph(f"<b>{headers[1]}</b>", body_style)
            ])
        for r in rows:
            data.append([
                Paragraph(r[0], body_style),
                Paragraph(r[1], body_style)
            ])
        ts = TableStyle([
            ("BACKGROUND",    (0,0), (-1,0), DARK_BLUE if headers else LIGHT_BLUE),
            ("TEXTCOLOR",     (0,0), (-1,0), white if headers else DARK_GREY),
            ("FONTNAME",      (0,0), (-1,0), "Helvetica-Bold"),
            ("FONTSIZE",      (0,0), (-1,-1), 9),
            ("ROWBACKGROUNDS", (0,1), (-1,-1), [white, LIGHT_GREY]),
            ("GRID",          (0,0), (-1,-1), 0.5, MID_GREY),
            ("LEFTPADDING",   (0,0), (-1,-1), 8),
            ("RIGHTPADDING",  (0,0), (-1,-1), 8),
            ("TOPPADDING",    (0,0), (-1,-1), 5),
            ("BOTTOMPADDING", (0,0), (-1,-1), 5),
            ("VALIGN",        (0,0), (-1,-1), "TOP"),
        ])
        return Table(data, colWidths=col_w, style=ts, hAlign="LEFT")

    def three_col_table(rows, headers=None):
        col_w = [4*cm, 6*cm, 6.5*cm]
        data = []
        if headers:
            data.append([Paragraph(f"<b>{h}</b>", body_style) for h in headers])
        for r in rows:
            data.append([Paragraph(cell, body_style) for cell in r])
        ts = TableStyle([
            ("BACKGROUND",    (0,0), (-1,0), DARK_BLUE),
            ("TEXTCOLOR",     (0,0), (-1,0), white),
            ("FONTNAME",      (0,0), (-1,0), "Helvetica-Bold"),
            ("FONTSIZE",      (0,0), (-1,-1), 8.5),
            ("ROWBACKGROUNDS", (0,1), (-1,-1), [white, LIGHT_GREY]),
            ("GRID",          (0,0), (-1,-1), 0.5, MID_GREY),
            ("LEFTPADDING",   (0,0), (-1,-1), 7),
            ("RIGHTPADDING",  (0,0), (-1,-1), 7),
            ("TOPPADDING",    (0,0), (-1,-1), 5),
            ("BOTTOMPADDING", (0,0), (-1,-1), 5),
            ("VALIGN",        (0,0), (-1,-1), "TOP"),
        ])
        return Table(data, colWidths=col_w, style=ts, hAlign="LEFT")

    # ════════════════════════════════════════════════════════════════════════
    # CONTENT BUILD
    # ════════════════════════════════════════════════════════════════════════
    story = []

    # ── TITLE CARD ───────────────────────────────────────────────────────────
    title_table = Table(
        [[Paragraph("De Novo Drug Design", title_style)],
         [Paragraph("Comprehensive Study Notes", subtitle_style)],
         [Paragraph("Pharmacology & Medicinal Chemistry  |  July 2026", date_style)]],
        colWidths=[17*cm],
        style=TableStyle([
            ("BACKGROUND", (0,0), (-1,-1), DARK_BLUE),
            ("TOPPADDING",    (0,0), (-1,-1), 18),
            ("BOTTOMPADDING", (0,0), (-1,-1), 14),
            ("LEFTPADDING",   (0,0), (-1,-1), 12),
            ("RIGHTPADDING",  (0,0), (-1,-1), 12),
            ("ROUNDEDCORNERS", [6]),
        ])
    )
    story.append(title_table)
    story.append(Spacer(1, 0.5*cm))

    # ── quick-definition box ─────────────────────────────────────────────────
    story.append(coloured_box(
        "De novo drug design is a computational approach to generating entirely new chemical "
        "structures optimised for a biological target — without requiring a previously known "
        "active compound as a starting point. The term <i>de novo</i> (Latin: 'from the new') "
        "distinguishes this from ligand-based optimisation of existing hits.",
        bg=LIGHT_BLUE, border=MID_BLUE, label="Definition"
    ))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 1. INTRODUCTION & BACKGROUND
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("1.  Introduction & Historical Background")

    story.append(Paragraph(
        "Traditional drug discovery relied almost entirely on experimental high-throughput screening "
        "(HTS) of compound libraries or empirical chemical modification of natural products. "
        "Although powerful, HTS is resource-intensive and limited by the chemical diversity of "
        "existing libraries. Computational de novo design emerged in the early 1990s as a way to "
        "explore the vastly larger <i>virtual</i> chemical space — estimated at 10<super>60</super> "
        "drug-like molecules — far beyond what any physical collection could cover.", body_style))

    story += section_header("Key milestones", level=2)
    milestones = [
        ("1990s", "First rule-based fragment-assembly programs (LUDI, LEGEND, SPROUT)"),
        ("2000s", "QSAR models and pharmacophore-guided design become routine"),
        ("2010s", "Machine learning (ML) begins replacing hand-coded scoring functions"),
        ("2017–2020", "Deep generative models (VAEs, GANs, RNNs on SMILES) transform the field"),
        ("2020–present", "Graph neural networks, diffusion models, and large language models (LLMs) "
                         "enable 3-D structure-aware, multi-property optimisation; >3,000 AI-assisted "
                         "drugs in development pipelines as of early 2025"),
    ]
    story.append(two_col_table(milestones, headers=["Era", "Development"]))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 2. DRUG DISCOVERY PIPELINE CONTEXT
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("2.  Where De Novo Design Fits in the Discovery Pipeline")

    pipeline_steps = [
        ("Target Identification", "Identify disease-relevant protein/RNA target (genomics, proteomics, CRISPR screens)"),
        ("Target Validation", "Confirm target's role; obtain 3-D crystal/cryo-EM structure or AlphaFold model"),
        ("Hit Discovery", "<b>De novo design generates novel hit scaffolds here</b> (also: HTS, fragment-based, virtual screening)"),
        ("Hit-to-Lead", "Iterative computational + synthetic optimisation of potency, selectivity, ADMET"),
        ("Lead Optimisation", "Fine-tune PK, safety, formulation; de novo methods assist scaffold hopping"),
        ("Preclinical / Clinical", "In vitro, animal, then human studies — de novo design less central beyond this point"),
    ]
    story.append(two_col_table(pipeline_steps, headers=["Stage", "Role of De Novo Design"]))
    story.append(Spacer(1, 0.3*cm))

    story.append(coloured_box(
        "<b>Key distinction:</b>  De novo design targets the hit-discovery and lead-optimisation stages. "
        "It is complementary — not a replacement — for HTS, fragment-based methods, and experimental assays.",
        bg=GREEN_BOX, border=GREEN_BORDER
    ))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 3. CORE APPROACHES
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("3.  Core Computational Approaches")

    # 3A
    story += section_header("3A.  Ligand-Based De Novo Design", level=2)
    story.append(Paragraph(
        "Used when 3-D target structure is unavailable but multiple active ligands exist. "
        "The method extracts pharmacophoric features — hydrogen-bond donors/acceptors, hydrophobic "
        "patches, charged groups, aromatic rings — from known actives and generates new molecules "
        "that satisfy those features.", body_style))
    story.append(bullet("<b>Molecular fingerprints:</b> encode substructure presence/absence as bit-vectors; "
                        "similarity metrics (Tanimoto coefficient) guide analogue generation."))
    story.append(bullet("<b>Shape/electrostatic similarity:</b> compares 3-D molecular envelopes and "
                        "charge distributions irrespective of bond topology."))
    story.append(bullet("<b>Pharmacophore models:</b> abstract spatial arrangements of key interaction "
                        "groups; used to filter or seed generative models."))
    story.append(bullet("<b>QSAR/QSPR:</b> quantitative structure–activity/property relationships "
                        "predict biological activity or ADMET from chemical descriptors; trained models "
                        "guide inverse design."))

    story += section_header("3B.  Structure-Based De Novo Design (SBDD)", level=2)
    story.append(Paragraph(
        "Requires a 3-D structure of the target binding site (X-ray, cryo-EM, or AlphaFold2/3 model). "
        "New molecules are grown or placed inside the pocket to maximise favourable interactions "
        "while minimising clashes.", body_style))

    story += section_header("Fragment Assembly / Growing", level=3)
    story.append(bullet("Fragments (small, simple scaffolds) are docked into the active site."))
    story.append(bullet("Linkers connect fragments, or chemical groups are iteratively appended to "
                        "explore available pocket space."))
    story.append(bullet("Classic tools: LUDI (1992), GLIDE fragment-linking, FBDD pipelines."))

    story += section_header("Molecular Docking + Scoring", level=3)
    story.append(bullet("Generated molecules are docked: software samples thousands of "
                        "poses (positions + conformations) in the rigid or flexible receptor."))
    story.append(bullet("Scoring functions estimate binding free energy (force-field, empirical, or ML-based)."))
    story.append(bullet("GPU-accelerated molecular dynamics (MD) refines binding poses and "
                        "captures receptor flexibility — including cryptic allosteric pockets."))

    story += section_header("Virtual Screening", level=3)
    story.append(bullet("De novo generated libraries are screened computationally before any synthesis."))
    story.append(bullet("Structure-based VS uses docking; ligand-based VS uses fingerprint/pharmacophore matching."))
    story.append(bullet("AlphaFold-derived structures have expanded SBDD to previously 'undruggable' proteins."))
    story.append(Spacer(1, 0.2*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 4. AI / MACHINE LEARNING METHODS
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("4.  AI & Deep Learning Methods for De Novo Design")

    story.append(Paragraph(
        "The fusion of deep learning with computational chemistry has revolutionised de novo design. "
        "Rather than relying on hand-coded rules, these models learn molecular grammars and "
        "property–structure relationships directly from data.", body_style))

    ai_methods = [
        ("Recurrent Neural\nNetworks (RNNs)\non SMILES",
         "Sequential character generation of SMILES strings. Pretrained on large chemical databases "
         "(ChEMBL, ZINC) then fine-tuned on actives via transfer learning.",
         "Early practical generative models; fast; limited 3-D awareness"),
        ("Variational\nAutoencoders\n(VAEs)",
         "Encode molecules into a continuous latent space; decode points in that space to new molecules. "
         "Enables smooth interpolation and property-guided gradient descent.",
         "Generates drug-like molecules; latent-space traversal intuitive for chemists"),
        ("Generative\nAdversarial\nNetworks (GANs)",
         "Generator creates molecules; discriminator judges drug-likeness. Adversarial training improves "
         "chemical realism over successive iterations.",
         "High-quality outputs but training instability (mode collapse)"),
        ("Graph Neural\nNetworks (GNNs)",
         "Represent molecules as graphs (nodes = atoms, edges = bonds). Atom-by-atom or bond-by-bond "
         "generation preserves chemical validity explicitly.",
         "Strong 3-D and property modelling; used in DRAGONFLY, REINVENT"),
        ("Diffusion Models",
         "Iteratively denoise random 3-D point clouds into coherent molecular structures. "
         "Models like DiffSBDD, TargetDiff, GenMol (2025) generate ligands conditioned on "
         "protein pocket geometry.",
         "State-of-the-art for structure-based generation; captures complex distributions"),
        ("Transformer /\nLarge Language\nModels (LLMs)",
         "Treat SMILES or SELFIES as token sequences; attention mechanism captures long-range "
         "dependencies. Multi-modal models combine sequence + structural data.",
         "Strong zero-shot and few-shot generalisation; emerging in 2024–2026"),
        ("Reinforcement\nLearning (RL)",
         "Policy network generates molecules; reward function encodes desired properties "
         "(docking score, QED, synthetic accessibility, ADMET). REINVENT framework is widely used.",
         "Flexible multi-objective optimisation; risk of reward hacking"),
        ("Active Learning\n(AL)",
         "Iterative strategy: model identifies uncertain/novel molecules, selects them for "
         "experimental testing, updates training set. Uses Bayesian or ensemble uncertainty estimates.",
         "Efficient exploration with fewer experiments; replaces evolutionary algorithms"),
    ]

    ai_table_data = [[
        Paragraph("<b>Method</b>", body_style),
        Paragraph("<b>Mechanism</b>", body_style),
        Paragraph("<b>Notes / Strengths</b>", body_style)
    ]]
    for row in ai_methods:
        ai_table_data.append([Paragraph(cell, body_style) for cell in row])

    ai_table = Table(ai_table_data,
                     colWidths=[3.8*cm, 7.2*cm, 5.5*cm],
                     style=TableStyle([
                         ("BACKGROUND",    (0,0), (-1,0), DARK_BLUE),
                         ("TEXTCOLOR",     (0,0), (-1,0), white),
                         ("FONTNAME",      (0,0), (-1,0), "Helvetica-Bold"),
                         ("FONTSIZE",      (0,0), (-1,-1), 8.5),
                         ("ROWBACKGROUNDS", (0,1), (-1,-1), [white, LIGHT_GREY]),
                         ("GRID",          (0,0), (-1,-1), 0.5, MID_GREY),
                         ("LEFTPADDING",   (0,0), (-1,-1), 6),
                         ("RIGHTPADDING",  (0,0), (-1,-1), 6),
                         ("TOPPADDING",    (0,0), (-1,-1), 5),
                         ("BOTTOMPADDING", (0,0), (-1,-1), 5),
                         ("VALIGN",        (0,0), (-1,-1), "TOP"),
                     ]),
                     hAlign="LEFT")
    story.append(ai_table)
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 5. MOLECULAR REPRESENTATIONS
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("5.  Molecular Representations")

    rep_data = [
        ("SMILES", "Simplified Molecular Input Line Entry System — linear text encoding of molecular "
                   "structure (e.g. CCO = ethanol). Most widely used; simple but doesn't encode 3-D geometry."),
        ("SELFIES", "Self-referencing embedded strings — 100% syntactically valid alternative to SMILES; "
                    "every string decodes to a valid molecule. Preferred for generative models."),
        ("Molecular Graphs", "Atoms as nodes, bonds as edges with attributes. Processed by GNNs; "
                              "preserves chemical topology naturally."),
        ("3-D Point Clouds", "Atomic coordinates in 3-D space. Input for diffusion and equivariant "
                              "neural networks (e.g. SE(3)-transformers). Captures conformation."),
        ("Fingerprints", "ECFP (Morgan fingerprint), MACCS keys — fixed-length binary or count vectors "
                          "encoding substructural features. Used in similarity searches and QSAR."),
        ("Descriptors", "Physicochemical properties (MW, logP, TPSA, HBD, HBA, rotatable bonds) "
                         "computed from structure. Lipinski Ro5 uses these for drug-likeness filters."),
    ]
    story.append(two_col_table(rep_data, headers=["Representation", "Description"]))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 6. DRUG-LIKENESS & OPTIMISATION CRITERIA
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("6.  Drug-Likeness & Multi-Property Optimisation")

    story.append(Paragraph(
        "Generated molecules must satisfy multiple overlapping criteria simultaneously — "
        "a major challenge distinct from simply optimising a single docking score.", body_style))

    story += section_header("Lipinski Rule of Five (Ro5)", level=2)
    story.append(Paragraph("Empirical guidelines for oral bioavailability (Lipinski et al., 2001):", body_style))
    ro5 = [
        "Molecular weight ≤ 500 Da",
        "LogP (lipophilicity) ≤ 5",
        "H-bond donors ≤ 5",
        "H-bond acceptors ≤ 10",
        "Note: many exceptions exist (e.g. macrocycles, natural products, beyond-Ro5 space)",
    ]
    for item in ro5:
        story.append(bullet(item))

    story += section_header("Key Optimisation Targets", level=2)
    opt_data = [
        ("Potency (IC50/Ki)", "Target binding affinity — usually the primary docking/scoring objective"),
        ("Selectivity", "Avoid off-target binding; crucial for safety (e.g. hERG channel avoidance)"),
        ("ADMET", "Absorption, Distribution, Metabolism, Excretion, Toxicity — predicted by QSAR/ML models"),
        ("Synthetic Accessibility (SA)", "SA score, SYBA, FSscore — penalise synthetically inaccessible structures"),
        ("Novelty & Diversity", "Metrics ensure generated compounds differ sufficiently from known actives"),
        ("QED Score", "Quantitative Estimate of Drug-likeness — single 0–1 score combining multiple Ro5-related properties"),
        ("TPSA", "Topological Polar Surface Area — correlates with membrane permeability and CNS penetration"),
        ("Metabolic Stability", "Predicted microsomal clearance; bioisostere swaps improve stability"),
    ]
    story.append(two_col_table(opt_data, headers=["Property", "Relevance"]))
    story.append(Spacer(1, 0.3*cm))

    story.append(coloured_box(
        "<b>Multi-objective optimisation:</b>  In practice, RL reward functions combine weighted sums "
        "of docking score + QED + SA score + ADMET predictions. Pareto-front methods identify "
        "compound sets where no single property can improve without degrading another.",
        bg=LIGHT_BLUE, border=MID_BLUE
    ))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 7. NOTABLE TOOLS & PLATFORMS
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("7.  Notable Tools, Frameworks & Platforms")

    tools_data = [
        ("REINVENT 4", "AstraZeneca (open-source)", "RL + transfer learning; widely used SMILES-based generative framework"),
        ("DRAGONFLY", "Interactome deep learning", "GNN + language model; zero-shot structure-based design (Nature Comms 2024)"),
        ("DiffSBDD / TargetDiff", "Academic", "Diffusion-based pocket-conditioned 3-D ligand generation"),
        ("GenMol", "NVIDIA + collaborators", "Discrete diffusion generalist model; drug discovery (arXiv 2025)"),
        ("Glide / Schrödinger", "Schrödinger Inc.", "Industry-standard docking; integrated ML scoring (FEP+)"),
        ("AutoDock Vina", "Open-source", "Fast, widely used free docking engine for virtual screening"),
        ("AlphaFold3", "Google DeepMind", "Predicts protein + ligand complex structures; expands SBDD targets"),
        ("ChEMBL / ZINC", "EMBL-EBI / UCSF", "Public databases of bioactive molecules; primary training data sources"),
        ("RDKit", "Open-source (Python)", "Core cheminformatics library: descriptor calculation, fingerprints, filtering"),
    ]
    story.append(three_col_table(tools_data, headers=["Tool", "Source", "Role in De Novo Design"]))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 8. EVALUATION METRICS
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("8.  Evaluating Generative Models")

    story.append(Paragraph(
        "Evaluating de novo design models requires metrics beyond simple accuracy because the goal "
        "is novel, valid, diverse, and drug-like molecules — not just reproducing training data.", body_style))

    metrics_data = [
        ("Validity", "Fraction of generated SMILES that are chemically valid (parseable by RDKit). "
                     "Modern models achieve >95% with SELFIES or constrained decoding."),
        ("Uniqueness", "Fraction of valid generated molecules that are non-duplicate. High uniqueness "
                        "indicates diverse output."),
        ("Novelty", "Fraction of unique molecules not present in training set. High novelty = genuine "
                     "exploration beyond known chemical space."),
        ("Drug-likeness (QED)", "Average QED score of generated molecules. QED ~0.67 is typical of "
                                  "marketed oral drugs."),
        ("Diversity", "Mean pairwise Tanimoto dissimilarity of generated molecules. Low diversity "
                       "indicates mode collapse."),
        ("FCD (Fréchet\nChemNet Distance)", "Measures statistical distance between distributions of "
                                              "generated and reference molecules in a learned latent space. "
                                              "Lower = more similar to real drug space."),
        ("Docking Score", "Mean or top-percentile docking score of generated molecules against the "
                           "target. Primary metric for SBDD models."),
        ("Hit Rate", "Fraction of generated molecules passing all multi-property thresholds "
                      "(activity, ADMET, SA). Ultimate practical metric."),
    ]
    story.append(two_col_table(metrics_data, headers=["Metric", "Meaning"]))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 9. VALIDATED EXAMPLES
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("9.  Experimentally Validated Examples")

    story.append(Paragraph(
        "Several AI-designed compounds have progressed into preclinical or clinical development:", body_style))

    validated = [
        ("DSP-1181\n(OCD)", "Exscientia × Sumitomo Dainippon", "First AI-designed drug to enter Phase I clinical trial (2020). "
                              "RNN-generated 5-HT1A partial agonist. Designed in ~12 months vs typical 4–5 years."),
        ("CDK inhibitors\n(Cancer)", "Insilico Medicine", "Generative chemistry + RL used to design novel CDK6 inhibitors; "
                                      "ISM001 for IPF entered Phase II trials (2022)."),
        ("PCSK9 inhibitors", "Academic + Pharma collaborations",
         "De novo generated macrocyclic peptides inhibiting PCSK9 for hypercholesterolaemia; "
         "validated by SPR and cellular assays."),
        ("JAK inhibitors", "Various AI-first biotechs",
         "GNN-generated JAK1/2 selective inhibitors with improved ADMET profiles vs marketed drugs."),
        ("Anti-tuberculosis\n(DprE1)", "DRAGONFLY study\n(Nature Comms 2024)",
         "Zero-shot deep interactome learning generated novel DprE1 inhibitor scaffolds; "
         "confirmed by crystallography. Demonstrated no prior ligand data needed."),
    ]
    validated_table_data = [[
        Paragraph("<b>Compound / Target</b>", body_style),
        Paragraph("<b>Team</b>", body_style),
        Paragraph("<b>Outcome</b>", body_style)
    ]]
    for row in validated:
        validated_table_data.append([Paragraph(cell, body_style) for cell in row])
    validated_table = Table(validated_table_data,
                            colWidths=[3.5*cm, 4.5*cm, 8.5*cm],
                            style=TableStyle([
                                ("BACKGROUND",    (0,0), (-1,0), ACCENT_TEAL),
                                ("TEXTCOLOR",     (0,0), (-1,0), white),
                                ("FONTNAME",      (0,0), (-1,0), "Helvetica-Bold"),
                                ("FONTSIZE",      (0,0), (-1,-1), 8.5),
                                ("ROWBACKGROUNDS", (0,1), (-1,-1), [white, LIGHT_GREY]),
                                ("GRID",          (0,0), (-1,-1), 0.5, MID_GREY),
                                ("LEFTPADDING",   (0,0), (-1,-1), 7),
                                ("RIGHTPADDING",  (0,0), (-1,-1), 7),
                                ("TOPPADDING",    (0,0), (-1,-1), 5),
                                ("BOTTOMPADDING", (0,0), (-1,-1), 5),
                                ("VALIGN",        (0,0), (-1,-1), "TOP"),
                            ]), hAlign="LEFT")
    story.append(validated_table)
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 10. CHALLENGES & LIMITATIONS
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("10.  Challenges & Limitations")

    story.append(coloured_box(
        "Despite rapid progress, de novo AI drug design remains a field with substantial unresolved challenges. "
        "Most successes have been in scaffold optimisation rather than truly breakthrough de novo discovery.",
        bg=RED_BOX, border=RED_BORDER
    ))
    story.append(Spacer(1, 0.15*cm))

    challenges = [
        ("<b>Synthetic accessibility:</b> Generative models readily produce exotic structures that are "
         "practically impossible to synthesise. SA filtering and retrosynthesis predictors (SYNTHIA, "
         "ASKCOS) are now integrated into pipelines.", False),
        ("<b>Data scarcity:</b> High-quality experimental bioactivity data for many novel targets is "
         "limited; models trained on public databases (ChEMBL) may not generalise.", False),
        ("<b>Reward hacking:</b> RL models can exploit weaknesses in surrogate scoring functions, "
         "producing molecules that score well computationally but are inactive in vitro.", False),
        ("<b>3-D accuracy:</b> Even AlphaFold3 structures may contain binding-site errors; docking "
         "scores derived from inaccurate pockets are unreliable.", False),
        ("<b>ADMET prediction gaps:</b> In vitro–in vivo discordance means computational ADMET "
         "predictions frequently fail in animal studies.", False),
        ("<b>Interpretability:</b> Deep generative models are largely black boxes; chemists cannot "
         "always rationalise why a specific structural motif was proposed.", False),
        ("<b>Benchmarking:</b> No standardised, universally agreed benchmarks exist for comparing "
         "de novo design methods head-to-head against traditional approaches.", False),
        ("<b>Regulatory acceptance:</b> Regulatory frameworks (FDA, EMA) for AI-generated drug "
         "candidates remain nascent; documentation and validation requirements are evolving.", False),
    ]
    for text, sub in challenges:
        story.append(bullet(text, sub))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 11. FUTURE DIRECTIONS
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("11.  Future Directions (2025–2030 Outlook)")

    future = [
        "<b>Hybrid quantum-classical computing:</b> Quantum algorithms for molecular simulation may "
        "dramatically accelerate exact free-energy calculations for binding affinity.",
        "<b>Digital twins:</b> Patient-specific in silico models combined with generative AI will "
        "enable personalised drug design matched to individual genomic profiles.",
        "<b>Multi-modal foundation models:</b> LLMs trained simultaneously on sequences, structures, "
        "bioassay data, and clinical outcomes will enable 'zero-shot' design for any target.",
        "<b>Automated synthesis planning:</b> Deep integration of retrosynthetic AI (AiZynthFinder, "
        "ASKCOS) with generative design will close the loop from idea to synthesisable molecule "
        "in a single pipeline.",
        "<b>Closed-loop robotic laboratories:</b> AI design → automated synthesis → high-throughput "
        "assay → model update cycles running without human intervention (self-driving labs).",
        "<b>Larger clinical trial datasets:</b> As more AI-designed drugs advance to Phase II/III, "
        "outcome data will enable training of 'clinical success'-optimising models.",
    ]
    for item in future:
        story.append(bullet(item))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 12. COMPARISON: TRADITIONAL vs AI DE NOVO
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("12.  Traditional vs. AI-Assisted De Novo Design")

    compare_data = [
        ("Chemical space explored", "~10⁶–10⁷ (HTS library)", "~10¹⁰–10²⁰ (virtual / latent space)"),
        ("Hit identification time", "Months–years", "Days–weeks"),
        ("Structural novelty", "Constrained to library scaffolds", "Truly novel scaffolds accessible"),
        ("Multi-property optim.", "Sequential, iterative", "Simultaneous, reward-guided"),
        ("Synthetic accessibility", "Guaranteed (purchasable)", "Requires explicit SA constraint"),
        ("Interpretability", "High (chemist-driven)", "Low (black-box models)"),
        ("Data dependence", "Minimal (phenotypic OK)", "High-quality SAR data needed"),
        ("Cost (computational)", "Low (screening only)", "High GPU/cloud compute for training"),
        ("Cost (experimental)", "High (large compound libraries)", "Lower (fewer compounds tested)"),
    ]
    compare_table_data = [[
        Paragraph("<b>Criterion</b>", body_style),
        Paragraph("<b>Traditional / HTS</b>", body_style),
        Paragraph("<b>AI De Novo Design</b>", body_style)
    ]]
    for row in compare_data:
        compare_table_data.append([Paragraph(cell, body_style) for cell in row])
    compare_table = Table(compare_table_data,
                          colWidths=[5*cm, 5.5*cm, 6*cm],
                          style=TableStyle([
                              ("BACKGROUND",    (0,0), (-1,0), DARK_BLUE),
                              ("TEXTCOLOR",     (0,0), (-1,0), white),
                              ("FONTNAME",      (0,0), (-1,0), "Helvetica-Bold"),
                              ("FONTSIZE",      (0,0), (-1,-1), 8.5),
                              ("ROWBACKGROUNDS", (0,1), (-1,-1), [white, LIGHT_GREY]),
                              ("GRID",          (0,0), (-1,-1), 0.5, MID_GREY),
                              ("LEFTPADDING",   (0,0), (-1,-1), 7),
                              ("RIGHTPADDING",  (0,0), (-1,-1), 7),
                              ("TOPPADDING",    (0,0), (-1,-1), 5),
                              ("BOTTOMPADDING", (0,0), (-1,-1), 5),
                              ("VALIGN",        (0,0), (-1,-1), "TOP"),
                          ]), hAlign="LEFT")
    story.append(compare_table)
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # 13. QUICK REVISION SUMMARY
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("13.  Quick Revision Summary")

    summary_points = [
        "De novo drug design creates novel chemical structures from scratch, targeting a specific molecular objective.",
        "Two main paradigms: <b>ligand-based</b> (pharmacophore, QSAR, fingerprints) and <b>structure-based</b> (docking, MD, fragment assembly).",
        "AI methods — RNNs, VAEs, GANs, GNNs, diffusion models, LLMs — learn generative models of chemical space.",
        "Reinforcement learning combines generation + property optimisation into a single training loop.",
        "Key representations: SMILES, SELFIES, molecular graphs, 3-D point clouds, fingerprints, descriptors.",
        "Multi-property optimisation balances potency, selectivity, ADMET, QED, and synthetic accessibility simultaneously.",
        "Evaluation: validity, uniqueness, novelty, diversity, FCD, docking score, hit rate.",
        "AlphaFold3 structures + diffusion models = current frontier of structure-based de novo design.",
        "Major challenges: SA, data scarcity, reward hacking, ADMET gaps, interpretability, benchmarking.",
        "Validated examples: DSP-1181 (OCD, Phase I), ISM001 (IPF, Phase II), DRAGONFLY DprE1 inhibitors.",
        "Future: self-driving labs, digital twins, quantum computing integration, multi-modal foundation models.",
    ]
    for i, pt in enumerate(summary_points, 1):
        story.append(Paragraph(f"<b>{i}.</b>  {pt}", bullet_style))
    story.append(Spacer(1, 0.3*cm))

    # ══════════════════════════════════════════════════════════════════════
    # REFERENCES
    # ══════════════════════════════════════════════════════════════════════
    story += section_header("References")

    refs = [
        "Goodman & Gilman's The Pharmacological Basis of Therapeutics (14th ed., 2023). "
        "Chapter 1: Drug Discovery — Computer-Aided Drug Discovery, pp. 312–430.",
        "Schneider G & Fechner U. Computer-based de novo design of drug-like molecules. "
        "Nat Rev Drug Discov. 2005;4:649–663.",
        "Tang Y, Moretti R, Meiler J. Recent Advances in Automated Structure-Based De Novo Drug Design. "
        "J Chem Inf Model. 2024;64(6):1936–1955. PMID: 38485516.",
        "Gangwal A, Lavecchia A. Unleashing the power of generative AI in drug discovery. "
        "Drug Discov Today. 2024;29(6). PMID: 38663579.",
        "Özelik R et al. Generative Deep Learning for de Novo Drug Design — A Chemical Space Odyssey. "
        "J Chem Inf Model. 2025. PMID: 40632942.",
        "Ozdemir ES et al. Deep generative molecular design and its value in modern drug discovery. "
        "Expert Opin Drug Discov. 2026. PMID: 41778634.",
        "Lavecchia A. Advancing drug discovery with deep attention neural networks. "
        "Drug Discov Today. 2024;29(8). PMID: 38925473.",
        "Stokes JM et al. A deep learning approach to antibiotic discovery (halicin). Cell. 2020;181(2):475–483.",
        "Luo S et al. Prospective de novo drug design with deep interactome learning. "
        "Nat Commun. 2024;15:3408. [DRAGONFLY]",
        "Frontiers in Hematology. De novo drug design through artificial intelligence: an introduction. "
        "Front Hematol. 2024;3:1305741.",
        "Lipinski CA et al. Experimental and computational approaches to estimate solubility and "
        "permeability in drug discovery. Adv Drug Deliv Rev. 2001;46:3–26.",
    ]
    for ref in refs:
        story.append(Paragraph(f"• {ref}", ref_style))

    story.append(Spacer(1, 0.4*cm))
    story.append(HRFlowable(width="100%", thickness=1, color=MID_GREY))
    story.append(Spacer(1, 0.1*cm))
    story.append(Paragraph(
        "Generated by Orris Medical AI  |  July 14, 2026  |  For educational use only — not a clinical guideline.",
        caption_style))

    # ── BUILD ────────────────────────────────────────────────────────────────
    def on_page(canvas, doc):
        canvas.saveState()
        # header stripe
        canvas.setFillColor(DARK_BLUE)
        canvas.rect(0, A4[1]-1*cm, A4[0], 1*cm, fill=1, stroke=0)
        canvas.setFillColor(white)
        canvas.setFont("Helvetica-Bold", 8)
        canvas.drawString(2*cm, A4[1]-0.65*cm, "De Novo Drug Design — Study Notes")
        canvas.setFont("Helvetica", 8)
        canvas.drawRightString(A4[0]-2*cm, A4[1]-0.65*cm, "Orris Medical AI  |  July 2026")
        # footer
        canvas.setFillColor(LIGHT_GREY)
        canvas.rect(0, 0, A4[0], 0.9*cm, fill=1, stroke=0)
        canvas.setFillColor(DARK_GREY)
        canvas.setFont("Helvetica", 7.5)
        canvas.drawCentredString(A4[0]/2, 0.35*cm, f"Page {doc.page}")
        canvas.restoreState()

    doc.build(story, onFirstPage=on_page, onLaterPages=on_page)
    print(f"PDF created: {OUTPUT}")

build_pdf()