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The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations

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Abstract

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

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Data availability

All of the data upon which the work described here is based have been drawn from the open literature available. The specific datasets need to used are provided in the Supplemental Information.

Code availability

ADMET Predictor® and GastroPlus® are commercially available from Simulations Plus, Inc., Lancaster CA 93534 (https://www.simulations-plus.com). Access for non-commercial use is available through the company’s academic collaborators program.

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Acknowledgements

We would like to thank Eric Martin for his editorial input to this article as well as for the invaluable feedback on the program provided by him and his colleagues at Novartis.

Funding

Development of AIDD was internally funded by Simulations Plus, Inc. Access to a pre-release version of ADMET Predictor 10.4 was provided to RDC as part of Simulation Plus’ academic collaboration program.

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Authors and Affiliations

  1. Simulations Plus, Inc., 42505 10th Street West, Lancaster, CA, 93534‑7059, USA

    Jeremy Jones, Michael S. Lawless, David W. Miller & Marvin Waldman

  2. The Indiana University Luddy School of Informatics, Computing and Engineering, 700 N. Woodlawn Avenue, Bloomington, IN, 47408, USA

    Robert D. Clark

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  1. Jeremy Jones
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  2. Robert D. Clark
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  3. Michael S. Lawless
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Contributions

MW conceived of AIDD and was primarily responsible for its implementation. He received key programming and infrastructure support from DWM. RDC and MSL contributed application support and feedback during the program’s development. RDC conceived and carried out the TzP case study. He and JJ worked together on the analysis of the results; all authors reviewed the manuscript and provided editorial input.

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Correspondence to Jeremy Jones.

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Conflict of interest

MSL, DSM and JJ are currently employed by Simulations Plus, Inc., and own stock in the company or options on the stock thereof. RDC and MW are former employees but still hold stock in the company. The authors declare no competing interests beyond those implicit in their employment by their respective institutions. All authors are current or former employees of Simulations Plus, Inc., which distributes the software described in this manuscript.

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Jones, J., Clark, R.D., Lawless, M.S. et al. The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations. J Comput Aided Mol Des 38, 14 (2024). https://doi.org/10.1007/s10822-024-00552-6

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