The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out

• URL: http://arxiv.org/abs/2501.05457v1
• Date: Tue, 24 Dec 2024 15:41:37 GMT
• Title: The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out
• Authors: Rıza Özçelik, Francesca Grisoni,
• Abstract summary: We take a fresh $- textitcritical$ and $textitconstructive -$ perspective on de novo design evaluation. We systematically investigate widely used evaluation metrics and expose key pitfalls ('traps') that were previously overlooked. Our results are expected to provide a new lens for evaluating the de novo designs proposed by generative deep learning approaches.
• Score: 0.0
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• Abstract: "How to evaluate de novo designs proposed by a generative model?" Despite the transformative potential of generative deep learning in drug discovery, this seemingly simple question has no clear answer. The absence of standardized guidelines challenges both the benchmarking of generative approaches and the selection of molecules for prospective studies. In this work, we take a fresh $- \textit{critical}$ and $\textit{constructive} -$ perspective on de novo design evaluation. We systematically investigate widely used evaluation metrics and expose key pitfalls ('traps') that were previously overlooked. In addition, we identify tools ('treasures') and strategies ('ways out') to navigate the complex 'jungle' of generative drug discovery, and strengthen the connections between the molecular and deep learning fields along the way. Our systematic and large-scale results are expected to provide a new lens for evaluating the de novo designs proposed by generative deep learning approaches.

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