Epik: pKa and Protonation State Prediction through Machine Learning

Johnston, Ryne C.; Yao, Kun; Kaplan, Zachary; Chelliah, Monica; Leswing, Karl; Seekins, Sean; Watts, Shawn; Calkins, David R.; Elk, Jackson Chief; Jerome, Steven V.; Repasky, Matthew P.; Shelley, John C.

doi:10.26434/chemrxiv-2023-c6z8t

Cited by 19 publications

(36 citation statements)

References 25 publications

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“…54 Consistently, the DMAM group pK a 's in 16, 18, and 27 were estimated as 8.64, 8.62, and 8.69 using Schrodinger's Epikx program. 55 Therefore, to emphasize the protonated form, we denote these compounds as compounds 16H, 18H, and 27H in Table 1. Next, we compared the reaction barriers of 15−18.…”

Section: ωB97x-d3(bj)-calculated Reaction Barriers Are Highly Predict...mentioning

confidence: 99%

Quantum Descriptors for Predicting and Understanding the Structure–Activity Relationships of Michael Acceptor Warheads

Liu

Vázquez-Montelongo

et al. 2023

J. Chem. Inf. Model.

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Predictive modeling and understanding of chemical warhead reactivities have the potential to accelerate targeted covalent drug discovery. Recently, the carbanion formation free energies as well as other ground-state electronic properties from density functional theory (DFT) calculations have been proposed as predictors of glutathione reactivities of Michael acceptors; however, no clear consensus exists. By profiling the thiol-Michael reactions of a diverse set of singly- and doubly-activated olefins, including several model warheads related to afatinib, here we reexamined the question of whether low-cost electronic properties can be used as predictors of reaction barriers. The electronic properties related to the carbanion intermediate were found to be strong predictors, e.g., the change in the Cβ charge accompanying carbanion formation. The least expensive reactant-only properties, the electrophilicity index, and the Cβ charge also show strong rank correlations, suggesting their utility as quantum descriptors. A second objective of the work is to clarify the effect of the β-dimethylaminomethyl (DMAM) substitution, which is incorporated in the warheads of several FDA-approved covalent drugs. Our data suggest that the β-DMAM substitution is cationic at neutral pH in solution and promotes acrylamide’s intrinsic reactivity by enhancing the charge accumulation at Cα upon carbanion formation. In contrast, the inductive effect of the β-trimethylaminomethyl substitution is diminished due to steric hindrance. Together, these results reconcile the current views of the intrinsic reactivities of acrylamides and contribute to large-scale predictive modeling and an understanding of the structure–activity relationships of Michael acceptors for rational TCI design.

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Section: ωB97x-d3(bj)-calculated Reaction Barriers Are Highly Predict...mentioning

confidence: 99%

Quantum Descriptors for Predicting and Understanding the Structure–Activity Relationships of Michael Acceptor Warheads

Liu

Vázquez-Montelongo

et al. 2023

J. Chem. Inf. Model.

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“…From these, 5 representative scaffolds were selected for the SHAMAN simulations, namely benzene (BENX), dihydro-pyrido-pyrimidinone-midazo-pyridine (BENF), benzothiophene (BETH), methyl-pyrimidine (MEPY), and piperazine (PIRZ). The preparation and comparison of the HARIBOSS and RBIND libraries was done using a KNIME 4.6 protocol that includes the following steps: i) molecule preparation using Epik 86 at pH 7.4, ii) conversion to canonical SMILES using RDkit v. 2022.3, iii) Murcko scaffold derivation using the RDkit Murcko Scaffolds KNIME node, iv) set comparison using the ‘Compare Ligand Sets’ node provided by Schrodinger v. 2022.3, and finally v) a fragmentation of the common scaffolds using the RECAP fragmentation method 87 (implemented as the ‘Fragments from Molecules’ node provided by Schrodinger). All probes used in the SHAMAN simulations have been prepared using the LigPrep module of Schrodinger Suite 88 at pH 7.4.…”

Section: Methodsmentioning

confidence: 99%

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

Panei

Gkeka

Bonomi

2023

Preprint

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The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Mostin silicotools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identified all the experimentally resolved pockets and ranked them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

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“…47 Prior to IFD, the designed compounds were energy optimized using LigPrep in Schrodinger software with OPLS3 force field, 48 resulting in the generation of corresponding 3D conformations. Subsequently, the Epik algorithm 49 was employed for ionization handling at a pH value of 7.0 ± 2.0. The target protein structure was retrieved from the PDB database.…”

Section: ■ Conclusionmentioning

confidence: 99%

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design

Gou,

Yang,

Guo

et al. 2024

J. Chem. Inf. Model.

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Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

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Epik: pKa and Protonation State Prediction through Machine Learning

Cited by 19 publications

References 25 publications

Quantum Descriptors for Predicting and Understanding the Structure–Activity Relationships of Michael Acceptor Warheads

Quantum Descriptors for Predicting and Understanding the Structure–Activity Relationships of Michael Acceptor Warheads

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design

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