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I am a PhD student at Loschmidt Labs, where our primary focus lies in protein engineering. My current project revolves around analyzing molecular dynamics through the utilization of machine learning and explainable AI methods.
Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for pr...
Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein struc...
Identifying drug-target interactions through computational methods is raised an important and key step in the process of drug discovery and drug-oriented research during the last years. In addition to the advantages of existing computational methods, there are also challenges that affect methods' efficiency and provide obstacles in the direction of...
Drug-target interactions prediction is an essential prerequisite for drug discovery and human medicine. Computational drug-target interactions prediction methods are low-cost alternatives to laboratory activities. One of the most popular computational methods for drug-target interactions prediction is bipartite local models (BLM). This method uses...
Background: Prediction of drug-target interactions is an essential step in drug discovery. Given a drug-target interactions network, the objective of this task is to predict probable missing edges from known interactions. Computationally predicting drug-target interactions is an appropriate alternative for the time-consuming and the costly experime...