SkyNet For Drugs: A Novel User-Centric Approach to Drug Discovery
Abstract
Traditional drug discovery is an expensive and laborious multi-step process that requires a detailed understanding of disease pathobiology, potential drug target characterization, synthesis, experimental evaluation, and optimization of putative drug candidates as a pretext for clinical evaluation which often does not translate into success. With the advent of whole genome sequencing, machine learning, and artificial intelligence, drug discovery and development are now enhanced both in speed and precision. SkyNet For Drugs (Skynet4D) is an AI-driven platform that automates the preclinical processes by integrating drug-target retrieval based on patient's diagnostic medical reports, and chemical database/ADMETox screening leading to the selection of a potential drug candidate. Skynet4D framework utilizes a combination of Natural Language Processing (NLP-GenAI), custom molecular docking tools (DiffDock®-GNINA®) for high-throughput virtual screening (HTVS), and an AI-enabled ADMETox (ADMETAi®) tool to generate drug candidates with a high chance of being effective within clinical settings. In this paper, the proof-of-concept for Skynet4D was presented with a medical diagnostic report of a COVID-19 patient; leading to the prediction of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‑CoV‑2) main protease, betaCoV_Nsp5_Mpro as putative targets. The target sequences retrieval, 3D structures retrieval/generation, putative ligand search (COCONUT, LOTUS, ChEMBL, etc.), and ranking…
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