Publications by authors named "Andrew E Voronkov"
Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising target in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors are currently known, identification of their novel chemotypes attracts considerable interest. In this study, the molecular docking and machine learning-based virtual screening techniques combined with the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) profile prediction and molecular dynamics simulations were applied to a subset of the ZINC database containing about 1.
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- Molecular dynamics simulations help us understand molecular systems, and we're using machine learning to enhance this by treating interaction data as 2D tensors over time.
- We applied these techniques to predict ligand-protein affinity, specifically for tankyrase, a target in colorectal cancer treatment, achieving good accuracy with convolutional neural networks.
- Our approach shows promise as an efficient tool for virtual screening in drug discovery, thanks to improved computational power and the method's low complexity.
One of the major challenges in the current drug discovery is the improvement of the docking-based virtual screening performance. It is especially important in the rational design of compounds with desired polypharmacology or selectivity profiles. To address this problem, we present a methodology for the development of target-specific scoring functions possessing high screening power.
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