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Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.
Danilack, Aaron D; Dickson, Callum J; Soylu, Cihan; Fortunato, Mike; Rodde, Stephane; Munkler, Hagen; Hornak, Viktor; Duca, Jose S.
Afiliação
  • Danilack AD; Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA. aaron.danilack@novartis.com.
  • Dickson CJ; Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA.
  • Soylu C; Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA.
  • Fortunato M; Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA.
  • Rodde S; Biomedical Research, Novartis, Novartis Campus, 4056, Basel, Switzerland.
  • Munkler H; Technical Research & Development, Novartis Pharma AG, Novartis Campus, 4056, Basel, Switzerland.
  • Hornak V; Merck Research Laboratories, 33 Avenue Louis Pasteur, Boston, MA, 02115, USA.
  • Duca JS; Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA.
J Comput Aided Mol Des ; 38(1): 21, 2024 May 01.
Article em En | MEDLINE | ID: mdl-38693331
ABSTRACT
Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Teoria Quântica / Desenho de Fármacos / Cisteína / Aprendizado de Máquina Limite: Humans Idioma: En Revista: J Comput Aided Mol Des Assunto da revista: BIOLOGIA MOLECULAR / ENGENHARIA BIOMEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Teoria Quântica / Desenho de Fármacos / Cisteína / Aprendizado de Máquina Limite: Humans Idioma: En Revista: J Comput Aided Mol Des Assunto da revista: BIOLOGIA MOLECULAR / ENGENHARIA BIOMEDICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos