ARTIFICIAL INTELLIGENCE-ASSISTED DRUG DESIGN COMBINED WITH MOLECULAR DOCKING FOR PRIORITIZATION OF POTENTIAL LEAD MOLECULES ACROSS FIVE THERAPEUTIC TARGETS
TL;DR
The integrated workflow supports computational narrowing of chemical space and target-specific lead prioritization and should be considered computational leads requiring biochemical, cellular, pharmacokinetic and toxicity validation before any therapeutic claim is made.
Abstract
Background: Artificial intelligence-assisted drug discovery can reduce the chemical search space by integrating molecular generation, drug-likeness assessment and structure-based docking. However, computational rankings require careful validation and should be interpreted as prioritization rather than evidence of therapeutic efficacy. Objectives: To integrate AI-assisted molecular design, physicochemical/ADMET screening, molecular docking and ligand–protein interaction analysis for identification of potential leads across selected cancer, metabolic and neurodegenerative targets. Methods: A documented computational workflow was applied to five ligand-bound human targets—EGFR (1M17), KRAS G12C (5V71), DPP-4 (1X70), PTP1B (1C83) and AChE (4EY7). The supplied thesis dataset described progressive filtering of a 1350-member virtual library to 14 final candidates, followed by drug-likeness/ADMET assessment, AutoDock Vina docking and redocking validation. Candidate prioritization integrated docking score, target-relevant interactions and computational pharmacokinetic/toxicity characteristics. Results: All five reference systems passed the predefined redocking criterion of <2.0 Å, with RMSD values of 0.65–1.14 Å. The prioritized leads were CAN-AI-01 (EGFR, −9.71 kcal/mol), KRA-AI-01 (KRAS G12C, −9.12 kcal/mol), DIA-AI-01 (DPP-4, −9.35 kcal/mol), PTP-AI-01 (PTP1B, −8.84 kcal/mol) and NEU-AI-01 (AChE, −12.42 kcal/mol). All five showed zero reported Lipinski violations, high predicted gastrointestinal absorption, negative Ames predictions and no predicted hERG inhibition or hepatotoxicity in the supplied screening dataset. Conclusions: The integrated workflow supports computational narrowing of chemical space and target-specific lead prioritization. The identified molecules should be considered computational leads requiring biochemical, cellular, pharmacokinetic and toxicity validation before any therapeutic claim is made.