Title: Avoiding Drug Failures: Right First Time; Fast First Time

Abstract:  We explore how state-of-the-art molecular simulation can be used to train advanced neural networks for high-fidelity, high-accuracy forcefields from Density-Functional Simulation. This allows - indeed, liberates and empowers, us - to train more accurate force-fields and apply this to basin-hopping journeys through potential-energy landscapes, allowing us to revolutionise the prediction and validation of polymorph populations.

Biography:Following a First in Chemical Engineering in 2000 and Ph.D. on crystallisation molecular simulation in 2003 at UCD Chemical Engineering, Niall English carried out further simulation and theoretical studies on hydrates at the National Energy Technology Laboratory, a U.S. DOE research facility. During 2005 to 2007, he worked for the Chemical Computing Group in Cambridge, GB. Here, Niall developed molecular simulation codes, protocols and methods for biomolecular simulation, used chiefly in structure-based drug design applications in the pharmaceutical industry. Niall commenced his position as a lecturer in the School of Chemical and Bioprocess Engineering in January 2007, being promoted to senior lecturer in January 2014 and professor in February 2017. He is currently lecturing on chemical-engineering design, thermodynamics, experimental design, process control and molecular simulation. His research interests encompass nanoscience (such as nano-bubbles), energy and materials - gas hydrates, solar and renewable energies, and simulation of e/m field effects on (nano)materials and biological systems. He has a special interest in water, whether in the bulk or at interfaces or in (nano-)confined systems. He is also co-founder of two campus spin-out companies, BioSimulytics and Aqua-B.

Chief Technology OfficerBioSimulytics