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Molecular Dynamics Simulation

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Interpreting forces as deep learning gradients improves quality of predicted protein structures.

Biophysical journal
Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a c...

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors.

Journal of biomolecular structure & dynamics
Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are sear...

Discovery of potential RIPK1 inhibitors by machine learning and molecular dynamics simulations.

Physical chemistry chemical physics : PCCP
Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) plays a crucial role in inflammation and cell death, so it is a promising candidate for the treatment of autoimmune, inflammatory, neurodegenerative, and ischemic diseases. So far, there ...

Calculation of Protein Folding Thermodynamics Using Molecular Dynamics Simulations.

Journal of chemical information and modeling
Despite advances in artificial intelligence methods, protein folding remains in many ways an enigma to be solved. Accurate computation of protein folding energetics could help drive fields such as protein and drug design and genetic interpretation. H...

Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation.

Journal of biomolecular structure & dynamics
Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into...

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides.

Nature communications
Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low...

Generation of focused drug molecule library using recurrent neural network.

Journal of molecular modeling
CONTEXT: With the wide application of deep learning in drug research and development, de novo molecular design methods based on recurrent neural network (RNN) have strong advantages in drug molecule generation. The RNN model can be used to learn the ...

Virtual screening and invitro evaluation of cyclooxygenase inhibitors from using the machine learning tool.

Journal of biomolecular structure & dynamics
has a variety of compounds, and some of these compounds may have anti-inflammatory and antioxidant properties. In the present study, we identified the compounds in the leaf extract of through Gas Chromatography-Mass Spectrometry (GC-MS) analysis an...

Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks.

Biophysical journal
Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA s...

Application of Machine Learning Algorithms to Metadynamics for the Elucidation of the Binding Modes and Free Energy Landscape of Drug/Target Interactions: a Case Study.

Chemistry (Weinheim an der Bergstrasse, Germany)
In the context of drug discovery, computational methods were able to accelerate the challenging process of designing and optimizing a new drug candidate. Amongst the possible atomistic simulation approaches, metadynamics (metaD) has proven very power...