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Danilack AD, Dickson CJ, Soylu C, Fortunato M, Rodde S, Munkler H, Hornak V, Duca JS. Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design. J Comput Aided Mol Des 2024; 38:21. [PMID: 38693331 DOI: 10.1007/s10822-024-00560-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/25/2024] [Indexed: 05/03/2024]
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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Affiliation(s)
- Aaron D Danilack
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA.
| | - Callum J Dickson
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Cihan Soylu
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Mike Fortunato
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA
| | - Stephane Rodde
- Biomedical Research, Novartis, Novartis Campus, 4056, Basel, Switzerland
| | - Hagen Munkler
- Technical Research & Development, Novartis Pharma AG, Novartis Campus, 4056, Basel, Switzerland
| | - Viktor Hornak
- Merck Research Laboratories, 33 Avenue Louis Pasteur, Boston, MA, 02115, USA
| | - Jose S Duca
- Biomedical Research, Novartis, 181 Massachusetts Avenue, Cambridge, MA, 02139, USA
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Alcázar JJ. Thiophene Stability in Photodynamic Therapy: A Mathematical Model Approach. Int J Mol Sci 2024; 25:2528. [PMID: 38473777 DOI: 10.3390/ijms25052528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Thiophene-containing photosensitizers are gaining recognition for their role in photodynamic therapy (PDT). However, the inherent reactivity of the thiophene moiety toward singlet oxygen threatens the stability and efficiency of these photosensitizers. This study presents a novel mathematical model capable of predicting the reactivity of thiophene toward singlet oxygen in PDT, using Conceptual Density Functional Theory (CDFT) and genetic programming. The research combines advanced computational methods, including various DFT techniques and symbolic regression, and is validated with experimental data. The findings underscore the capacity of the model to classify photosensitizers based on their photodynamic efficiency and safety, particularly noting that photosensitizers with a constant rate 1000 times lower than that of unmodified thiophene retain their photodynamic performance without substantial singlet oxygen quenching. Additionally, the research offers insights into the impact of electronic effects on thiophene reactivity. Finally, this study significantly advances thiophene-based photosensitizer design, paving the way for therapeutic agents that achieve a desirable balance between efficiency and safety in PDT.
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Affiliation(s)
- Jackson J Alcázar
- Centro de Química Médica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile
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Dudas B, Miteva MA. Computational and artificial intelligence-based approaches for drug metabolism and transport prediction. Trends Pharmacol Sci 2024; 45:39-55. [PMID: 38072723 DOI: 10.1016/j.tips.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 01/07/2024]
Abstract
Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.
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Affiliation(s)
- Balint Dudas
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France
| | - Maria A Miteva
- Université Paris Cité, CNRS UMR 8038 CiTCoM, Inserm U1268 MCTR, Paris, France.
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Madzhidov TI, Varnek A. Chemical reactions mining. Mol Inform 2022; 41:e2200044. [PMID: 35338606 DOI: 10.1002/minf.202200044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 03/25/2022] [Indexed: 11/12/2022]
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