AI Drug Discovery for Labs, Researchers & Students

Veridica AI accelerates drug discovery with predictive modeling, molecular generation, and multi-omics integration.

Accelerate Drug Discovery with AI-Powered Multi-Omics

Veridica integrates genomics, transcriptomics, proteomics, and phenotypic data with predictive models for potency, cytotoxicity, and selectivity. Models are based on transformer and graph baselines similar to ChemBERTa and Chemprop, fine tuned on public and proprietary datasets.

Rank candidates, reduce off target risk, and surface uncertainties before wet lab spend. Our Reproducibility Hub logs datasets, parameters, model versions, and QC so results are auditable for journals and investors.

Core AI Drug Discovery Features

IC50, EC50 & Ki Prediction

AI-powered binding affinity predictions for drug-target interactions. Perfect for medicinal chemistry research and academic studies.

Generative AI Drug Design

Generate novel drug candidates with optimized potency, selectivity, and safety profiles using advanced molecular generation algorithms.

Lab-Ready AI for Universities

Scalable AI tools for academic research labs, pharmaceutical R&D teams, and students learning computational drug discovery.

Why Modern Drug Discovery Needs AI

• ~90% of investigational drugs fail between Phase I and approval, wasting time and capital.
• US $2.23 billion—the average cost to bring one drug to market in 2024.
• 10–12 years from target discovery to first patient dose under traditional wet-lab pipelines.
• The AI drug-discovery market hit US $6.9 billion in 2025 and is growing at double-digit CAGR—digital acceleration is the new competitive edge.