CONTEXT: Traditionally, Hook. f. (Asteraceae) has been reported to be effective in cancer treatment which motivated the authors to explore the plant for novel anticancer compounds.
BACKGROUND: One of the main challenges in the early stages of drug discovery is the computational assessment of protein-ligand binding affinity. Machine learning techniques can contribute to predicting this type of interaction. We may apply these tec...
Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from ...
Protein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein inte...
Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the...
SAR and QSAR in environmental research
Sep 1, 2021
The acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitors play a key role in treating Alzheimer's disease. This study proposes an approach that integrates a modified binary particle swarm optimization (PSO) with a machine learning ...
Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free ...
Larger training datasets have been shown to improve the accuracy of machine learning (ML)-based scoring functions (SFs) for structure-based virtual screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, ha...
Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on n...
Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for...
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