Our computational drug discovery pipeline combines AI-powered virtual screening, property prediction, and generative molecular design to reduce the time and cost of bringing new therapeutics to market. A complete computational toolkit for every stage of the drug discovery journey 1 Target Identification: Identify and validate biological targets using computational methods, protein structure analysis, and literature-driven target prioritization. 2 Virtual Screening & Hit Identification: Screen millions of compounds in silico using structure-based (molecular docking with AutoDock Vina, RxDock) and ligand-based approaches to find promising hits. 3 ADMET & Toxicity Prediction: Predict absorption, distribution, metabolism, excretion, and toxicity properties early using validated AI/ML models — before costly synthesis. 4 De Novo Molecular Design: Generate novel, drug-like molecules using our hybrid reinforcement learning framework combining GPT-based generative models with multi-objective optimization and retrosynthetic feasibility analysis. 5 Lead Optimization: Multi-parameter optimization guided by machine learning to improve potency, selectivity, and drug-likeness of lead compounds.
