AI-Powered Predictive Analytics in Modern Drug Discovery: A Comparative Review of Current Tools and the Unique Advances of SwaLife Predictive Analytics

Abstract

Artificial intelligence (AI) has revolutionized drug discovery by enabling predictive analytics for binding affinity, ADMET properties, and toxicity, accelerating hit-to-lead optimization. Common tools such as SwissADME, pkCSM, DeepChem, ADMETlab, QikProp, and MolSoft provide valuable predictions but suffer from significant gaps including lack of integrated multi-site binding simulation, limited confidence scoring, and poor accommodation of natural product chemistries. This review critically compares these tools and highlights the unique advances of SwALife Predictive Analytics, an AI-powered platform that integrates multi-site binding affinity prediction, toxicity, and solubility assessments with explainable confidence metrics and natural product-friendly modeling. A case study on Rutin exemplifies SwALife’s interpretive power and actionable recommendations, positioning it as a transformative tool to reduce attrition and speed discovery.