Multi-omic data integration and causal inference to identify high-confidence druggable targets across disease areas.
Generative sequence models produce diverse antibody and protein therapeutic candidates conditioned on target structure.
Physics-informed binding affinity prediction and developability scoring to rank candidates before wet-lab validation.
Iterative multi-objective optimization of affinity, stability, immunogenicity, and manufacturability in closed-loop cycles.
Integrate transcriptomic, proteomic, and genetic data to prioritize targets with causal disease relevance and druggability scores.
Generate antibody sequences conditioned on 3D epitope structure using equivariant diffusion models trained on proprietary data.
Physics-informed neural networks predict binding free energy with sub-kcal/mol accuracy, validated against SPR data.
Bayesian optimization across affinity, stability, viscosity, and immunogenicity. Wet-lab results feed back in real time.
Predict aggregation propensity, clearance, and expression yield to de-risk candidates before entering CMC.
Visualize sequence landscapes, Pareto fronts, and campaign progress. Export reports for regulatory and partner discussions.
AbInitio's Therascript platform was incorporated into the Fingerprint consortium, a multi-institutional initiative leveraging agentic AI for Alzheimer's drug discovery. The team has advanced to the finals of the Alzheimer's Disease Data Initiative AI Prize.
AbInitio was selected for MIT's R2E program, which provides funding, mentorship, and resources to translate breakthrough biomedical research into scalable ventures.
AbInitio was chosen for the 5050 program by Fifty Years, a venture fund that backs founders building companies to solve the world's biggest problems through science and technology.
Join the pharma and biotech teams using Therascript to go from target to lead in weeks, not years.
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