Intology benchmarks its Locus autonomous R&D system across molecular drug discovery

Intology published an evaluation on 3 November 2026 applying its autonomous AI R&D platform Locus to three computational drug-discovery problems marked by small noisy datasets and costly simulations: molecular property prediction from scratch, low-data small-molecule potency adaptation and in silico antibody binder workflow design. Locus proposes hypotheses, runs parallel GPU experiments and updates its search strategy from validation feedback.

On a proprietary molecular property task where single-molecule CPU simulations take hours, Locus autonomously generated nearly 1 million simulation-labelled synthetic molecules and scaled custom architectures to billions of parameters, clearing the held-out test error target where Codex (GPT-6 Astra High) and Claude Code (Opus 5.5 Max) fell short. Adapting Boltz-2 across six ChEMBL IC50 targets (BChE, CYP19A1, EGFR, MAO-B, MAP4K1 and PLK4) under a 12 H100 GPU-hour cap per run, Locus achieved a 0.622 mean Spearman rank correlation on structurally and temporally held-out ligands, ahead of Codex (GPT-6 Astra Max, 0.569), Claude Code (Opus 5 Max, 0.450) and the closed Boltz-2.1 API (0.225), with smaller validation-test generalisation gaps.

On in silico antibody binder design scored by OpenDDE ipSAE (run with GPT-5.6 Sol High after biological safety classifiers blocked Opus and GPT-6 Astra), Locus improved BoltzGen/Boltz-2 and ESMC/ESMFold2 pipelines without fine-tuning weights, reaching 0.797 mean top-two ipSAE on ESM targets (0.758 Codex, 0.704 ESMFold2) and matching ESMFold2’s full-pool peak in 16.7% of the candidate budget (6 times more candidate-efficient). Intology’s 12 evaluation charts are rendered interactively in HTML; wet-lab validation and commercial pricing were not announced.