Computer-Aided Drug Discovery Patents

Computer-aided drug discovery CADD patents cover molecular docking and scoring functions; free energy perturbation FEP and molecular dynamics force fields; generative chemistry for de novo molecule design; phenotypic high-content screening HCS and ML-driven hit identification; protein structure prediction and protein-ligand interaction modeling; and ADMET in silico toxicity and pharmacokinetics prediction — with IP held by specialized software companies, pharmaceutical companies, and AI drug discovery startups: MAJOR CADD PATENT HOLDERS: SCHRÖDINGER: 500+; specific molecular simulation (specific specific FEP+ free energy perturbation: specific specific GPU-accelerated FEP from specific specific OPLS4 OPLS all-atom force field from specific specific torsional potential + specific specific polarizable charge model from specific specific protein-ligand relative binding free energy ΔΔG at specific specific <1 kcal/mol MAE mean absolute error from specific specific prospective validation for specific specific lead optimization from specific specific 0.5-1 kcal/mol accuracy from specific specific 100-1000× faster than specific specific experiment for specific specific GSK+Pfizer+BMS FEP+ partnership; specific specific Glide XP docking: specific specific extended-precision scoring function GScore from specific specific LigPrep+Protein Prep Wizard + specific specific XP GScore from specific specific hydrophobic enclosure + specific specific hydrogen bond + specific specific PhiPsi penalty for specific specific virtual screening 10^6 compound library for specific specific enrichment factor EF 10-50× at specific specific 1% database cutoff vs. specific specific random for specific specific active compound enrichment); RECURSION PHARMACEUTICALS: 200+; specific phenomics (specific specific high-content screening HCS: specific specific 6-million well weekly from specific specific CellPainting 1,536-well plate from specific specific 8-channel fluorescence confocal at specific specific 10× magnification from specific specific >1,000 features/cell from specific specific deep learning CNN cell morphology embedding for specific specific >250 GB data/week from specific specific ML active learning for specific specific phenotypic perturbation = specific specific target + specific specific compound = specific specific disease state from specific specific Recursion OS); INSILICO MEDICINE: 200+; specific generative CADD (specific specific Chemistry42 generative RL: specific specific REINVENT-style transformer from specific specific SMILES generation + specific specific multi-property reward from specific specific docking score + specific specific ADMET + specific specific QED drug-likeness for specific specific scaffold-constrained + specific specific scaffold-hop from specific specific QPLD quantum mechanics-polarized ligand docking from specific specific DFT B3LYP for specific specific INS018_055 fibrosis first AI-designed drug Phase 2 2023); DEEPMIND/GOOGLE (ALPHAFOLD): 300+; specific protein structure (specific specific AlphaFold2 protein structure: specific specific multi-sequence alignment MSA + specific specific pairwise distances from specific specific evolutionary covariation for specific specific Evoformer attention module from specific specific 48 iterations from specific specific >200M protein structures PDB+UniRef from specific specific TM-score >0.9 at specific specific <2Å backbone RMSD for specific specific >99% of human proteome at specific specific UniProt + specific specific structural biology replacement for specific specific solved proteins); ATOMWISE; EXSCIENTIA; BENEVOLENTAI; RELAY THERAPEUTICS: 300+ combined AI drug discovery.

Molecular docking innovations improving scoring function accuracy and computational throughput; FEP free energy perturbation innovations enabling faster lead optimization; and protein structure prediction and protein-ligand complex modeling innovations enabling structure-based drug design for previously undruggable targets represent three core CADD innovation domains: MOLECULAR DOCKING PATENTS: SCHRÖDINGER; OPENEYE; CCDC; TRIPOS/CERTARA: specific docking (specific specific induced-fit docking IFD: specific specific flexible receptor from specific specific receptor conformational sampling + specific specific softened potential from specific specific initial Glide docking at specific specific <1.5 kcal/mol penalty for specific specific receptor movement + specific specific Prime side-chain prediction for specific specific IFD-SP induced fit score for specific specific cryptic pocket + specific specific allosteric site vs. specific specific rigid receptor 20-40% pose improvement for specific specific flexible active sites like specific specific kinase DFG-in→out; specific specific covalent docking: specific specific reactive warhead from specific specific acrylamide Michael acceptor + specific specific nitrile + specific specific epoxide from specific specific CovDock algorithm for specific specific SMILES warhead perception + specific specific covalent docking score from specific specific covalent protein-ligand bond for specific specific irreversible inhibitor SARs like specific specific BTK EGFR covalent); FREE ENERGY CALCULATION PATENTS: SCHRÖDINGER; OPENFE; MIT; UCB PHARMA: specific FEP (specific specific relative binding FEP: specific specific thermodynamic cycle ΔΔGbind from specific specific RBFE relative binding free energy from specific specific λ-windows 12×100ps REST2 replica exchange for specific specific 1-3 day GPU compute on specific specific NVIDIA A100 GPU for specific specific 2-5 compound/day throughput from specific specific OPLS4 + specific specific Desmond MD engine for specific specific CYP3A4 substrate + specific specific kinase ATP-competitive inhibitor series; specific specific absolute solvation FEP: specific specific aqueous hydration free energy ΔGsolv from specific specific vacuum→water from specific specific RMSE 0.5-0.8 kcal/mol at specific specific 2500 compounds for specific specific LogP+pKa prediction from specific specific physical property control in specific specific medicinal chemistry); PROTEIN STRUCTURE PREDICTION PATENTS: DEEPMIND; BAKER LAB UW; ALPHAFOLD (ISOMORPHIC LABS): specific protein structure (specific specific AlphaFold2 Evoformer: specific specific MSA representation from specific specific pairwise distance residual features from specific specific depth 48 Evoformer + specific specific structure module SE(3) equivariant network from specific specific residue gas angles+positions for specific specific backbone RMSD <2Å GDT-TS >90 at specific specific CASP14 from specific specific TM-score >0.9 for specific specific >85% of structures; specific specific RoseTTAFold two-track 3D+1D: specific specific sequence+pair+orientation from specific specific 3-track attention for specific specific multi-chain complexes + specific specific protein-ligand complex binding from specific specific Baker lab IPD Institute for Protein Design; specific specific AlphaFold3/AlphaFill: specific specific protein-ligand atom-level prediction from specific specific diffusion model + specific specific ligand atom placement for specific specific de novo pocket prediction without specific specific prior co-crystal for specific specific structure-based drug design in specific specific previously un-crystallized targets).

Generative chemistry innovations for de novo molecule design satisfying multi-property constraints; ADMET absorption distribution metabolism excretion toxicity prediction model innovations; and active learning and Bayesian optimization innovations for efficient exploration of chemical space represent three additional CADD patent domains: GENERATIVE CHEMISTRY PATENTS: INSILICO; EXSCIENTIA; BENEVOLENTAI; PFIZER; NOVARTIS: specific generative chemistry (specific specific REINVENT scaffold RL: specific specific transformer SMILES generator from specific specific SMILES token sequence from specific specific RNN/transformer policy from specific specific multi-component reward from specific specific docking score + specific specific predicted pIC50 + specific specific ADMET score + specific specific QED + specific specific scaffold constraint for specific specific de novo molecular generation with specific specific 90%+ of generated molecules satisfying specific specific all >6 constraints simultaneously vs. specific specific <1% random from specific specific chemical space; specific specific graph neural network GNN molecular generation: specific specific junction tree VAE JTVAE from specific specific molecular graph from specific specific junction tree scaffold + specific specific subgraph attachment for specific specific valid molecule guarantee from specific specific continuous latent space from specific specific Bayesian optimization for specific specific scaffold optimization; specific specific diffusion-based molecule: specific specific DiffSBDD target-conditioned diffusion from specific specific 3D pocket geometry from specific specific Poisson surface reconstruction for specific specific generated molecule 3D coordinates + specific specific atom types for specific specific <0.5Å RMSD to specific specific crystal ligand pose at specific specific target pocket); ADMET PREDICTION PATENTS: CYPROTEX (EVOTEC); CHEMAXON; ACDLABS; OPTIBRIUM; SIMULATIONS PLUS: specific ADMET (specific specific ChemProp MPNN: specific specific message-passing neural network from specific specific atomic feature vector + specific specific edge feature from specific specific Morgan fingerprint + specific specific 3D pharmacophore for specific specific CYP450 inhibition IC50 + specific specific hERG cardiac safety + specific specific Caco-2 permeability at specific specific >90% AUC for specific specific classification vs. specific specific <2000 training set from specific specific active learning augmentation for specific specific rare toxicophore; specific specific multi-task ADMET: specific specific shared GNN backbone + specific specific task-specific head from specific specific 200+ ADMET endpoints from specific specific Tox21 + specific specific PCBA + specific specific ChEMBL from specific specific Chemprop multi-task for specific specific generalization to specific specific novel scaffolds with specific specific 50% fewer false negatives vs. specific specific single-task model); ACTIVE LEARNING CADD PATENTS: RECURSION; RELAY; SCHRÖDINGER; NOVARTIS: specific active learning (specific specific Bayesian optimization BO: specific specific Gaussian process GP from specific specific surrogate model pIC50(x) from specific specific acquisition function EI expected improvement from specific specific μ(x)+κσ(x) for specific specific next-experiment suggestion at specific specific 5-fold speedup vs. specific specific random screening from specific specific <200 synthesis+assay cycles to specific specific 10 nM lead from specific specific 10^6+ compound virtual library; specific specific multi-objective Pareto: specific specific EHVI expected hypervolume improvement from specific specific Pareto front from specific specific potency + specific specific selectivity + specific specific solubility + specific specific metabolic stability for specific specific simultaneous optimization for specific specific lead optimization from specific specific >4-property optimization in specific specific 50-100 cycles).

CADD startup IP strategy must recognize that the computational methods are often novel but face § 101 Alice risk in the US; understand that the drug candidates discovered using the platform are typically more easily patented than the platform algorithms themselves; navigate FTO against Schrödinger's large portfolio; and appreciate the unique role of proprietary datasets and partnerships with pharmaceutical companies as competitive moats that complement patent protection: AI DRUG DISCOVERY STARTUP IP STRATEGY: UNDERSTAND THE AI DRUG DISCOVERY IP LANDSCAPE: SCHRÖDINGER DOMINATES PHYSICS-BASED CADD: Schrödinger (500+) has the largest and most established CADD IP portfolio — FEP+, Glide docking, IFD, WaterMap — any startup using physics-based free energy or docking should conduct detailed FTO against Schrödinger's portfolio; DEEPMIND ALPHAFOLD PROTEIN STRUCTURE: DeepMind (300+) holds AlphaFold2 and AlphaFold3 patent applications — Isomorphic Labs (DeepMind spinout) commercializes AlphaFold for drug discovery; while AlphaFold2 code is open-source, system and method claims in patent applications may restrict commercial applications; EXSCIENTIA AND INSILICO HOLD GENERATIVE AI DRUG DISCOVERY IP: Exscientia (200+) and Insilico Medicine (200+) are the most patent-active pure-play AI drug discovery companies with focused portfolios in generative chemistry and AI-designed drug pipeline; RECURSION HOLDS PHENOMICS IP: Recursion (200+) has built a focused phenomics and HCS IP portfolio — the combination of CellPainting imaging + ML active learning + deep phenotypic screening is their core differentiation; AI DRUG DISCOVERY PATENTS FACE HIGH § 101 RISK: Pure AI/ML methods for drug discovery face significant Alice risk; the key is to anchor claims to specific experimental assay output (binding affinity measurement, cell viability measurement) or specific synthetic chemistry output (specific compound with measured property), not just algorithmic prediction; WHEN TO PATENT IN AI DRUG DISCOVERY: NOVEL DRUG CANDIDATE WITH DEMONSTRATED PRECLINICAL ACTIVITY — HIGHEST VALUE: The highest-value IP a CADD startup can produce is a novel drug candidate composition of matter patent with demonstrated in vitro + in vivo efficacy data — this is straightforwardly patentable and protects the core clinical asset; NOVEL ALGORITHM WITH SPECIFIC MEASURED PERFORMANCE ON VALIDATED DRUG DISCOVERY BENCHMARK: specific novel CADD method (specific specific model architecture + specific specific training data + specific specific optimization objective) with specific measured performance metric (specific specific Pearson r for FEP accuracy, specific specific enrichment factor EF@1% for docking, specific specific AUC for ADMET) vs. specific specific Schrödinger FEP+ or specific specific Glide XP baseline at specific specific same protein target class and specific specific same benchmark dataset — performance data at validated pharmaceutical benchmarks is essential to distinguish CADD algorithm claims from prior art; NOVEL GENERATIVE MODEL WITH MEASURED MULTI-PROPERTY SUCCESS RATE: specific novel generative chemistry system (specific specific architecture + specific specific reward function + specific specific training) with specific measured fraction of generated molecules satisfying ALL constraints simultaneously at specific specific molecular property thresholds + specific specific diversity measured by specific specific internal pairwise Tanimoto <0.4 at specific specific generation batch vs. specific specific REINVENT or specific specific JTVAE baseline; § 101 STRATEGY FOR CADD: the strongest CADD claims combine: (1) the computational method claim anchored to specific experimental assay data input + specific compound recommendation output; (2) the specific novel compound composition of matter claim; (3) the method-of-treatment claim if clinical data supports; this layered claim strategy gives redundancy even if computational-only claims face § 101 challenge; DATA AND PARTNERSHIP MOAT: proprietary integrated datasets (cellular phenomics + protein structure + bioactivity + clinical outcome) are not patentable but are extremely powerful competitive moats; exclusive technology partnership agreements with pharma companies (as Recursion, Insilico, and Exscientia have done) create additional barriers beyond patent protection; KEY FTO CHECKLIST: Schrödinger FEP+ OPLS4 GPU ΔΔGbind <1 kcal/mol GSK+Pfizer+BMS; Glide XP GScore hydrophobic enclosure EF 10-50× VHT virtual screening; IFD flexible receptor DFG-in/out covalent docking warhead acrylamide; Recursion CellPainting HCS 6M well/week 1,536-well 8-channel CNN phenomics; Insilico Chemistry42 REINVENT transformer scaffold-constrained RL INS018_055 Phase 2; DeepMind AlphaFold2 Evoformer MSA 48-iteration TM-score >0.9 <2Å RMSD; Baker lab RoseTTAFold 3-track protein-ligand complex; DiffSBDD target-conditioned diffusion 3D molecule generation pocket.