AI IN DRUG DISCOVERY
Survey of ML pipelines for molecular screening, target identification, and regulatory-aware validation in pharmaceutical R&D.
Problem
De Novo drug discovery is notoriously slow and expensive, typically boasting an 11% success rate and spanning 10 to 17 years. The massive datasets generated by genomics and molecular biology are too complex for traditional manual analysis, leading to high failure rates in clinical trials.
Architecture
A comprehensive integration of Deep Generative Models, Graph Neural Networks, and Reinforcement Learning applied across the pharmaceutical pipeline. From target identification and hit discovery to multi-objective ADMET optimization.
Clinical Relevance
AI models like graph-based deep learning and GANs reduce the "black box" problem via Explainable AI (XAI) and utilize Federated Learning for secure, cross-institutional collaboration without compromising patient privacy.
Proof
Presented at the National Conference on Recent Trends in Engineering Science and Technology (NCRTEST '25) and published via BookRivers. Successfully maps out the transition from traditional screening to AI-driven multi-omics personalization.
Mission Timeline
- Q3 2024: Research & Aggregation — Compiled data on classical vs AI-driven drug discovery pipelines.
- Q4 2024: Analysis — Evaluated recent success stories including Halicin and SARS-CoV-2 inhibitors.
- Q1 2025: Framework Design — Mapped the integration of Quantum Computing, XAI, and Federated Learning.
- Feb 2026: Publication — Full open-access manuscript presented at NCRTEST'25.