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Can I read Impact of Applicability Domains to Generative Artificial Intelligence on EtoBox?
Impact of Applicability Domains to Generative Artificial Intelligence by Maxime Langevin; Christoph Grebner; Stefan Güssregen; Susanne Sauer; Yi Li; Hans Matter; Marc Bianciotto is a Engineering article available to read on EtoBox.
What is Impact of Applicability Domains to Generative Artificial Intelligence about?
Molecular generative artificial intelligence is drawing significant attention in the drug design community, with several experimentally validated proof of concepts already published. Nevertheless, generative models are known for sometimes generating unrealistic, unstable, unsynthesizable, or uninteresting structures. This calls for methods to constrain those algorithms to generate structures in drug-like portions of the chemical space. While the concept of applicability domains for predictive models is well studied, its counterpart for generative models is not yet well-defined. In this work, we empirically examine various possibilities and propose applicability domains suited for generative models. Using both public and internal data sets, we use generative methods to generate novel structures that are predicted to be actives by a corresponding quantitative structure–activity relationships model while constraining the generative model to stay within a given applicability domain. Our work looks at several applicability domain definitions, combining various criteria, such as structural similarity to the training set, similarity of physicochemical properties, unwanted substructures, a
Who reads Impact of Applicability Domains to Generative Artificial Intelligence?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Maxime Langevin; Christoph Grebner; Stefan Güssregen; Susanne Sauer; Yi Li; Hans Matter; Marc Bianciotto
- Publisher
- American Chemical Society (ACS)
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)