Sandro Cosconati, PhD - "From Bench to Breakthrough: Democratizing AI-Powered Drug Discovery with PyRMD and PyRMD2Dock"

Sandro Cosconati, PhD
Full Professor
Head of the Medicinal Chemistry Unit
University of Campania Luigi Vanvitelli, Itlay

Short CV

Abstract
Artificial intelligence (AI) algorithms are revolutionizing drug discovery by enhancing the efficiency of various steps. However, their implementation often demands expertise in AI paradigms and coding, limiting accessibility to non-experts. Addressing this challenge, we introduce PyRMD, a Ligand-Based Virtual Screening (LBVS) tool powered by the random matrix discriminant (RMD) algorithm. PyRMD automates the screening process using target bioactivity data from ChEMBL, enabling rapid identification of ligands. With PyRMD, millions of compounds can be screened in hours, offering fine-tuning options and comprehensive benchmarking metrics.
PyRMD2Dock builds upon PyRMD's predictions by seamlessly integrating them with popular docking software for enhanced virtual screening throughput. PyRMD2Dock enables rapid screening of massive chemical databases, highlighting its predictive power and speed. These tools, freely available on GitHub, are a significant advancement in high-throughput virtual screening, propelling drug discovery forward.