BIreactive: A Machine-Learning Model to Estimate Covalent Warhead Reactivity
| Autor: | Ferruccio Palazzesi, Alexander Weber, Alexander Pautsch, Christofer S. Tautermann, Marc Grundl, Markus R. Hermann, Daniel Seeliger |
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| Rok vydání: | 2020 |
| Předmět: |
Acrylamides
010304 chemical physics Drug discovery General Chemical Engineering Ab initio Context (language use) General Chemistry Library and Information Sciences 01 natural sciences 0104 chemical sciences Computer Science Applications Machine Learning 010404 medicinal & biomolecular chemistry chemistry.chemical_compound chemistry Warhead Covalent bond Computational chemistry 0103 physical sciences Functional group Drug Discovery Molecule Reactivity (chemistry) Amino Acids |
| Zdroj: | Journal of chemical information and modeling. 60(6) |
| ISSN: | 1549-960X |
| Popis: | In the past decade, the pharmaceutical industry has paid closer attention to covalent drugs. Differently from standard noncovalent drugs, these compounds can exhibit peculiar properties, such as higher potency or longer duration of target inhibition with a potentially lower dosage. These properties are mainly driven by the reactive functional group present in the compound, the so-called warhead that forms a covalent bond with a specific nucleophilic amino-acid on the target. In this work, we report the possibility to combine ab initio activation energies with machine-learning to estimate covalent compound intrinsic reactivity. The idea behind this approach is to have a precise estimation of the transition state barriers, and thus of the compound reactivity, but with the speed of a machine-learning algorithm. We call this method "BIreactive". Here, we demonstrate this approach on acrylamides and 2-chloroacetamides, two warhead classes that possess different reaction mechanisms. In combination with our recently implemented truncation algorithm, we also demonstrate the possibility to use BIreactive not only for fragments but also for lead-like molecules. The generic nature of this approach allows also the extension to several other warheads. The combination of these factors makes BIreactive a valuable tool for the covalent drug discovery process in a pharmaceutical context. |
| Databáze: | OpenAIRE |
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