AI-Accelerated
Drug Discovery
Accelerate biologic drug development with foundation models that predict protein folding, optimize binding affinity, and design therapeutic proteins with enhanced efficacy and safety.
The Challenge of Biologic Drug Development
Biologic drugs, including monoclonal antibodies, enzyme replacement therapies, and engineered cytokines, represent the fastest-growing segment of the pharmaceutical industry. Yet developing a new biologic from target identification to clinical candidate typically takes 4 to 6 years and costs hundreds of millions of dollars, with failure rates exceeding 90% across all clinical stages.
The central challenge is optimizing multiple drug properties simultaneously. A therapeutic antibody must bind its target with high affinity and specificity, maintain stability during manufacturing and storage, avoid immunogenicity in patients, and achieve favorable pharmacokinetics. These properties are determined by the protein's sequence and structure, but the relationships between them are complex and often counterintuitive.
Current computational approaches address individual aspects of drug design, such as binding affinity prediction or stability estimation, but lack the integrated view needed to optimize the full set of drug-like properties. Experimental screening campaigns are expensive, slow, and can only test a tiny fraction of the available design space.
How Avitai Accelerates Drug Discovery
Our foundation models predict drug-like properties from sequence and optimize across multiple objectives simultaneously.
Target Identification
Mine biological knowledge bases to identify novel drug targets and validate their therapeutic potential through multi-modal evidence integration.
Affinity Optimization
Predict and optimize binding affinity between therapeutic proteins and their targets, identifying mutations that enhance potency while maintaining specificity.
Immunogenicity Prediction
Assess and reduce immunogenic risk by predicting T-cell epitopes and designing deimmunized variants that retain full therapeutic activity.
Developability Assessment
Predict manufacturability, aggregation propensity, and stability under formulation conditions early in the design process to avoid late-stage failures.
Foundation Models for Drug Design
Three models provide an integrated view from molecular sequence to therapeutic function.
Central Dogma Model
Predicts protein structure and function from genetic sequence, modeling how mutations affect folding, stability, and binding properties. Enables rational design of therapeutic proteins with optimized expression and post-translational modification profiles in production cell lines.
Learn moreDynamics Model
Simulates protein-target binding dynamics and conformational changes. Predicts binding kinetics (on-rate, off-rate), residence time, and how mutations affect the dynamic binding interaction, providing insights beyond static docking predictions.
Learn moreResearch Model
Mines clinical trial data, patent literature, and published studies to identify promising therapeutic targets, known liabilities, and successful design strategies from prior drug development campaigns. Accelerates the target validation phase.
Learn moreOpen-Source Tools for Drug Discovery
Accelerate your drug development pipeline with our JAX-native open-source ecosystem.
Generative models for de novo protein and antibody sequence design
Differentiable bioinformatics for sequence optimization and variant analysis
Neural operators for molecular dynamics and binding simulation
Knowledge extraction from drug discovery literature and clinical data
Accelerate Your Drug Development Pipeline
Reduce time-to-candidate and improve success rates with AI-powered biologic design.