AI Drug Discovery Patents

AI drug discovery patents cover target-identification and phenotypic-platform innovations; generative-chemistry and de-novo molecule-design innovations; physics-based and structure-based screening innovations; and the resulting composition-of-matter (the actual drug molecules) — with IP held by techbio platform companies, computational-chemistry firms, and AI-native biotechs. MAJOR AI-DRUG-DISCOVERY PATENT HOLDERS: RECURSION (200+): Recursion OS phenomics platform, high-content cellular imaging at scale, BioHive-2 supercomputer, maps of biology relating perturbations to morphological embeddings, target and indication discovery. INSILICO MEDICINE (150+): Pharma.AI — PandaOmics target identification from multi-omics, Chemistry42 generative chemistry (GAN, reinforcement learning, genetic algorithms for de novo design), lead candidate INS018_055 (ISM001-055) for idiopathic pulmonary fibrosis in Phase II. SCHRÖDINGER (300+): physics-based platform, FEP+ free-energy perturbation, WaterMap, Glide docking, active-learning ADMET, induced-fit. ATOMWISE (100+): AtomNet 3D convolutional neural network for structure-based virtual screening. OTHERS: Exscientia (Centaur Chemist active-learning design, DSP-1181), Isomorphic Labs (AlphaFold-derived structure prediction, DeepMind), BenevolentAI (knowledge graph), Generate Biomedicines (Chroma generative protein), Absci (generative antibody), Iambic, Cradle, Genesis Therapeutics.

Generative de-novo molecule-design innovations; target-identification and phenotypic-screening innovations; molecular-property and ADMET prediction innovations; and protein-structure and binding prediction innovations represent core AI-drug-discovery patent domains — though method claims face Alice/§101 abstract-idea scrutiny. GENERATIVE-CHEMISTRY PATENTS: variational autoencoder VAE latent-space molecule generation; generative adversarial network GAN and reinforcement-learning RL goal-directed design; diffusion and flow-based 3D molecule generation; scaffold-hopping and fragment-based generative design; multi-parameter optimization (potency + ADMET + synthesizability); retrosynthesis and synthetic-route planning (transformer, Monte Carlo tree search). TARGET-DISCOVERY PATENTS: multi-omics target identification, knowledge-graph relationship inference, phenotypic/morphological profiling (Cell Painting embeddings), CRISPR-screen + imaging integration, causal target-disease linkage. PROPERTY-PREDICTION PATENTS: graph neural network GNN molecular property prediction, ADMET (absorption, distribution, metabolism, excretion, toxicity) models, physics-based free-energy perturbation FEP for binding affinity, quantum-mechanics/ML hybrid potentials, solubility/permeability/hERG/CYP prediction. STRUCTURE PATENTS: protein structure prediction (AlphaFold-style attention/MSA), protein-ligand docking and pose prediction, binding-site detection, generative protein/antibody design (inverse folding, RFdiffusion-style), molecular dynamics ML force fields.

Composition-of-matter claims on the discovered molecule, formulation and method-of-treatment claims, and trade-secret protection of the platform itself represent the durable AI-drug-discovery IP — because pure ML/computational method claims are vulnerable under Alice/Mayo §101 as abstract ideas or natural phenomena. COMPOSITION-OF-MATTER IS THE DURABLE IP: the AI is a tool; the patentable, defensible asset is the NOVEL MOLECULE the AI discovers — claimed as a new chemical entity (Markush genus + specific species), with the same 20-year composition-of-matter protection any drug gets, regardless of how it was found; this is why Insilico's INS018_055 and Exscientia's candidates are protected as molecules, not as algorithms. METHOD-OF-TREATMENT AND FORMULATION: dosing regimens, combinations, patient-stratification (biomarker-defined populations), and formulations extend exclusivity. ALICE/§101 LIMITS PURE-ML CLAIMS: a claim to 'predict binding affinity using a neural network' risks being held an abstract idea (Alice) or a law of nature (Mayo) — to survive, claims need a specific technical improvement to the computer/process or integration into a practical application (a concrete assay, a specific architecture tied to a technical result). TRADE SECRET OFTEN BEATS PATENTING THE PLATFORM: the training data, model weights, and pipeline are frequently kept as trade secrets (no disclosure, no 20-year clock) rather than patented, because enforcement of an ML-method patent is hard and disclosure aids competitors.

AI drug discovery startup IP strategy must navigate Schrödinger physics-based computational patents (300+), Recursion phenomics-platform patents (200+), Insilico generative-chemistry patents (150+), Atomwise structure-based-screening patents, and a §101-constrained landscape where pure-ML method claims are weak — so the winning strategy is to PATENT THE MOLECULES (composition-of-matter) the platform produces while protecting the platform itself as trade secret, and to seek narrow, technically-grounded method claims only where there is a concrete improvement; identify whitespace in novel chemical entities against undrugged targets, generative-design architectures tied to specific technical results, and lab-in-the-loop active-learning systems with measured hit rates. AI-DRUG-DISCOVERY STARTUP IP STRATEGY: PATENT THE MOLECULE, TRADE-SECRET THE PLATFORM: composition-of-matter on the discovered new chemical entity is the durable 20-year asset; model weights, training data, and pipelines are usually better kept as trade secrets than disclosed in a §101-vulnerable method patent; METHOD CLAIMS MUST BE TECHNICALLY GROUNDED: to survive Alice/Mayo, claim a specific architecture producing a concrete technical improvement (a new assay, a measurable speedup, integration into a physical synthesis/screening loop), not 'use AI to design a drug'; NOVEL CHEMICAL MATTER AGAINST HARD TARGETS IS HIGHEST-VALUE: a new molecule hitting a previously-undrugged or 'undruggable' target (KRAS, protein-protein interfaces, molecular glues, targeted protein degraders) is the commercial prize; WHEN TO PATENT: NOVEL MOLECULE OR PLATFORM WITH MEASURED RESULT: file composition-of-matter the moment a lead chemotype is validated (structure + activity IC50/Ki + selectivity + ADMET); for platforms, patent only where there is a measured technical improvement (hit rate %, enrichment factor, prospective validation) vs. high-throughput-screening or FEP+ baseline — measured hit rate, prospective success, and binding affinity are the critical metrics; KEY FTO CHECKLIST: Schrödinger FEP+ free-energy-perturbation WaterMap Glide; Recursion phenomics Cell-Painting morphological-embedding maps-of-biology; Insilico Chemistry42 GAN/RL generative PandaOmics; Atomwise AtomNet 3D-CNN structure-based; Exscientia Centaur active-learning; Isomorphic/AlphaFold MSA-attention structure prediction; Generate/RFdiffusion generative protein; GNN ADMET retrosynthesis MCTS; Alice/Mayo §101 abstract-idea limits on pure-ML method claims; composition-of-matter Markush + method-of-treatment + formulation.