Publications by authors named "Jenke Scheen"
Article Synopsis
- * The NS3 helicase protein of ZIKV is a key target for drug development due to its role in viral replication, but challenges exist due to inadequate structural data for designing specific inhibitors.
- * High-throughput crystallographic fragment screening identified 46 fragments that bind to NS3, providing valuable 3D structures that can help design new antiviral drugs to combat ZIKV and other flaviviruses.
Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC and is expensive for large systems or large numbers of molecules. We propose a hybrid physical/graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations.
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- - The COVID Moonshot was a collaborative, open-science effort focused on finding a new drug to inhibit the SARS-CoV-2 main protease, which is crucial for the virus's survival.
- - Researchers developed a novel noncovalent, nonpeptidic inhibitor that stands out from existing drugs targeting the same protease, employing advanced techniques like machine learning and high-throughput structural biology.
- - Over 18,000 compound designs, 490 ligand-bound x-ray structures, and extensive assay data were generated and shared openly, creating a comprehensive and accessible knowledge base for future drug discovery efforts against coronaviruses.
A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration and requires a fraction of the training set size to outperform standalone FEP calculations.
View Article and Find Full Text PDFBest Practices for Alchemical Free Energy Calculations [Article v1.0].
Living J Comput Mol Sci
January 2020
Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly.
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