AIMC Topic: Molecular Dynamics Simulation

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Spatially Resolved Uncertainties for Machine Learning Potentials.

Journal of chemical information and modeling
Machine learning potentials have become an essential tool for atomistic simulations, yielding results close to ab initio simulations at a fraction of computational cost. With recent improvements on the achievable accuracies, the focus has now shifted...

Combining machine learning, molecular dynamics, and free energy analysis for (5HT)-2A receptor modulator classification.

Journal of molecular graphics & modelling
The 5-Hydroxytryptamine (5HT)-2A receptor, a key target in psychoactive drug development, presents significant challenges in the design of selective compounds. Here, we describe the construction, evaluation and validation of two machine learning (ML)...

Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase.

International journal of biological macromolecules
Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as directed evolution are often limited by the huge sequence space and combinatorial explosion, making it difficult to obtain optimal mutants. In recent yea...

Predicting Conformational Ensembles of Intrinsically Disordered Proteins: From Molecular Dynamics to Machine Learning.

The journal of physical chemistry letters
Intrinsically disordered proteins and regions (IDP/IDRs) are ubiquitous across all domains of life. Characterized by a lack of a stable tertiary structure, IDP/IDRs populate a diverse set of transiently formed structural states that can promiscuously...

Combined structure-based virtual screening and machine learning approach for the identification of potential dual inhibitors of ACC and DGAT2.

International journal of biological macromolecules
Acetyl-coenzyme A carboxylase (ACC) and diacylglycerol acyltransferase 2 (DGAT2) are recognized as potential therapeutic targets for nonalcoholic fatty liver disease (NAFLD). Inhibitors targeting ACC and DGAT2 have exhibited the capacity to reduce he...

Biomimetic fusion: Platyper's dual vision for predicting protein-surface interactions.

Materials horizons
Predicting protein binding with the material surface still remains a challenge. Here, a novel approach, platypus dual perception neural network (Platyper), was developed to describe the interactions in protein-surface systems involving bioceramics wi...

Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes.

Scientific reports
Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new c...

Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning.

Biophysical journal
Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥1 kbp) are challenging to address experimentally ...

Construction of a multi-tissue compound-target interaction network of Qingfei Paidu decoction in COVID-19 treatment based on deep learning and transcriptomic analysis.

Journal of bioinformatics and computational biology
The Qingfei Paidu decoction (QFPDD) is a widely acclaimed therapeutic formula employed nationwide for the clinical management of coronavirus disease 2019 (COVID-19). QFPDD exerts a synergistic therapeutic effect, characterized by its multi-component,...

Flavonoid as a Potent Antioxidant: Quantitative Structure-Activity Relationship Analysis, Mechanism Study, and Molecular Design by Synergizing Molecular Simulation and Machine Learning.

The journal of physical chemistry. A
In this work, a quantitative structure-antioxidant activity relationship of flavonoids was performed using a machine learning (ML) method. To achieve lipid-soluble, highly antioxidant flavonoids, 398 molecular structures with various substitute group...