Simulations Plus, Inc. (“Simulations Plus”), a leading provider of biosimulation, simulation-enabled performance and intelligence solutions, and medical communications to the biopharma industry, announced the award of a new research grant from the National Institutes of Health (NIH), secured in partnership with the University of Southern California (USC) Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences. The grant will be used to evaluate novel computational methods that account for water-ligand interactions in drug discovery and that integrate with the Artificial Intelligence-driven Drug Design (AIDD) module in ADMET Predictor ® to offer a first-of-its-kind ligand-based virtual screening (LBVS) solution for pharmaceutical companies.
For this award, Dr. Ian Haworth, Associate Professor and Vice Chair of Pharmacology and Pharmaceutical Sciences at the USC Mann School, and his lab will apply their previously developed algorithm (WATGEN) for the prediction of water positions in the unbound protein and protein-ligand complex. With support from the data scientists and software engineers at Simulations Plus, they will apply machine learning (ML) approaches to predict the pharmacophore features that will be used in ADMET Predictor’s proprietary 3D shape and feature matching algorithm.
“Identifying chemicals with shapes and characteristics similar to those that bind drug targets has been invaluable in drug discovery and development. However, the retention or displacement of water molecules during formation of the protein-ligand interface plays a significant role in determining ligand binding. This has often been overlooked in existing software programs, including LBVS algorithms,” said Dr. Noam Morningstar-Kywi , Scientist II at Simulations Plus and a key investigator for this grant. “Our goal is to develop new approaches that combine ML and validated 3D-based calculations to incorporate these essential water molecules into LBVS, enhancing current methods and enabling researchers to accelerate the discovery of better and more effective drugs.”
Dr. Haworth added, “We will harness the power of structure-based approaches, including the detailed information of protein-ligand and protein-water interactions, and combine them with the speed and accuracy associated with ligand-based similarity scoring methods. This project is a powerful collaboration between industry and academia that drives research from the lab into real-world applications, promising exciting, tangible results that could transform the field.”
The team at Simulations Plus will productize the updated methods into the ADMET Predictor platform and validate it by designing drugs against defined targets using the AIDD module. Selected compounds will be synthesized and tested experimentally to highlight the technology’s applications.
“As a drug discovery scientist, I am particularly excited to apply the NIH funding towards this innovative technology to design and test new compounds against several clinically relevant targets. We have the potential to dramatically reduce the Design-Make-Test-Analyze (DMTA) cycle of drug discovery,” said Dr. Jeremy Jones , Principal Scientist at Simulations Plus and principal investigator for this grant. “We are committed to driving impactful advancements that benefit our stakeholders and the global communities we serve, and we eagerly anticipate future collaborations that continue to create value and foster growth.”
The information presented in this press release is supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R43GM156103. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
