An AI Framework for Target-Based Lead Optimization: The SwALife Approach

Abstract

The increasing demand for rapid and cost-effective drug discovery necessitates the integration of artificial intelligence (AI) into traditional computational chemistry workflows. The SwALife Target & Lead Optimizer represents an advanced AI-assisted platform that facilitates protein -ligand interaction analysis, lead molecule optimization, and pharmacokinetic evaluation. By combining protein structure data (PDB format) and molecular descriptors (SMILES/InChIKey), the tool enables iterative optimization of small molecules to enhance their binding affinity, drug-likeness, and bioavailability. This paper presents the architecture, methodology, and case study outcomes demonstrating the efficiency of SwALife in optimizing drug-like compounds against target proteins.