You are cordially invited to attend the seminar organized by the Department of Chemistry.
Title: Computational and AI-driven Strategies for Next-Generation Antibiotic Discovery
Speaker: Dr. Hafiz Saqib Ali
Date: 04/11/2025, Tuesday
Time: 12:30 (Turkiye Time)
This is an online seminar. To request event details please send a message to department.
Computational and AI-driven Strategies for Next-Generation Antibiotic Discovery
Antimicrobial Resistance (AMR) is a critical global health crisis, linked to 5 million deaths in 2019. Without effective intervention, the WHO warns AMR could cause 10 million deaths anually by 2050. Current antibiotics are primarily natural products or derivatives, and their discovery is hindered by repeated rediscovery of identical molecules/classes, and the high costs of screening large synthetic chemical libraries. Despite extensive efforts, these traditional methods have not produced new antibiotics with broad clinical utility. Therefore, innovative approaches in antibiotic discovery are urgently needed to combat the escalating threat of AMR.
Advances in structural biology, computational chemistry and artificial intellegence (AI) offer powerful tools for the design of novel antibiotic chemotypes capable of evading key resistance mechanisms. I develop and employ state-of-the-art computational and AI strategies to design novel bioactive molecules and engineer enzymes with enhanced production of pharmaceutical compounds to tackle AMR. Recently, I have developed a structure-based virtual screening workflow capable of evaluating millions of compounds to identify inhibitors against tetracycline inactivating (TetX) enzymes, leading to the discovery of the first potent TetX inhibitor. In addition to that I have developed ML and advanced deep learning (DL) models to design inhibitors against β-lactamases including both serine β-lactamases and metallo β-lactamases. Together, these computational innovations exemplify the power of computational chemistry and AI in accelerating antibiotic discovery and biocatalyst design to address one of the most urgent global health challenges.
Short Biography:
Dr. Hafiz Saqib Ali completed his MPhil degree from Government College University Faisalabad (GCUF) and was awarded the prestigious Chief Minister Merit Scholarship (CMMS) by the Government of Pakistan to pursue his PhD studies at one of the top 50 universities in the world. He joined The University of Manchester, United Kingdom, in early 2018 and became part of the Manchester Institute of Biotechnology, working with Professor Richard Henchman and Professor Samuel de Visser. His PhD research focused on developing algorithms to determine the reaction kinetics and stability of biomolecules. After obtaining his PhD in early 2021, Dr. Ali began his postdoctoral career in Professor Amanda Jarvis’s group at the University of Edinburgh, where he worked on the design of novel copper-based artificial metalloenzymes (ArMs). Since 2022, he has been working as a Computational Scientist at the Ineos Oxford Institute for Antimicrobial Research (IOI), University of Oxford, collaborating with Professor Christopher Schofield and Professor Fernanda Duarte. His current research focuses on developing computational and machine learning strategies to identify new bioactive molecules to tackle antimicrobial resistance and to elucidate their mechanisms of action.
Over a ten-year period (2011–2021), Dr. Ali was awarded three scholarships, fully funding his undergraduate, master’s, and PhD studies. Additionally, he secured two research grants, including the CCPBioSim Software Project from the EPSRC (EP/T026301/1) in collaboration with STFC, to generalize the software CodeEntropy for biomolecular systems, and the HECBioSim grant, which provided 47,520 GPU hours on the BEDE supercomputing cluster. Dr. Ali has also established a high-quality research portfolio with 52 peer-reviewed publications in leading journals such as JACS, ACS Catalysis, Chemical Science, and the European Journal of Medicinal Chemistry, including 16 as first author and 6 as corresponding author. His work has achieved an H-index of 17 and an i10-index of 29, reflecting his growing influence and contribution to the scientific community.