Editorial: Machine Learning Methods in QSAR ModellingNo Abstractdoi:10.1002/qsar.200390046NoneQsar & Combinatorial ScienceSchneider, G. & Downs, G. (2003). Machine learning methods in QSAR modelling. QSAR Comb Sci. 22: 485-486.
DeepAutoQSAR is a machine learning (ML) solution that allows users to predict molecular properties based on chemical structure. The automated, supervised learning pipeline enables both novice and experienced users to train and inference best-in-class quantitative structure activity/property relationship (...
事实上,对已发表文献的分析表明,化学数据和数据库的持续增长,特别是在公共领域,刺激了QSAR文章发表数量的同时增长。QSAR建模已经在世界各地的学院、行业和政府机构中广泛应用。最近的观察表明,经过多年来基于结构的方法的强大优势,基于统计的QSAR方法在帮助指导药物优化方面的价值正开始被几个较大的cadd组的领导人所赞...
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration of analysis, prediction and discovery the key theme in accelerated alloy research. Advances in machine-learning methods and...
Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that...
& Hong, S. MetaDTA: meta-learning-based drug-target binding affinity prediction. In Proc. ICLR2022 Machine Learning for Drug Discovery (eds Katja, H. et al.) (ICLR, 2022). Olier, I. et al. Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Mach. ...
Utilizing machine learning-based QSAR model to overcome standalone consensus docking limitation in beta-lactamase inhibitors screening: a proof-of-concept study Thanet Pitakbut Jennifer Munkert Gregor Fuhrmann Research Open access 20 December 2024 Article: 249 Molecular exploration of natural and synt...
As the fields of artificial intelligence and machine learning are exploding, their universal nature is becoming more apparent. Machine learning is being leveraged in a huge variety of sub-fields, and…
In subject area: Biochemistry, Genetics and Molecular Biology Clustering is an unsupervised machine learning for data mining that divides datasets into different clusters based on similarity to reveal the inherent properties of data (Ay et al., 2023). ...
Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization...