Xujun Zhang

Feb 10, 2024 | pubs.acs.org | Xujun Zhang |Chao Shen |Haotian Zhang |Yu Kang

ConspectusMolecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein–ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths.

Jul 4, 2023 | pubs.rsc.org | Tingjun Hou |Chao Shen |Xujun Zhang |Chang-Yu Hsieh

A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers Tingjun Hou, Chao Shen, Xujun Zhang, Chang-Yu Hsieh, Yafeng Deng, Dong Wang, Lei Xu, Jian Wu, Dan Li, Yu Kang and Peichen Pan Abstract Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions...

May 4, 2023 | dx.doi.org | Xujun Zhang |Chao Shen |Tianyue Wang |Yu Kang

Machine-learning-based scoring functions (MLSFs) have gained attention for their potential to improve accuracy in binding affinity prediction and structure-based virtual screening (SBVS) compared to classical SFs. Developing accurate MLSFs for SBVS requires a large and unbiased dataset that includes structurally diverse actives and decoys. Unfortunately, most datasets suffer from hidden biases and data insufficiency.