Computational drug discovery
By Sisheng Ouyang, Junyan Lu, Xiangqian Kong, Zhongjie Liang, Cheng Luo, Hualiang Jiang. Acta Pharmacologica Sinica, 2012
306 citations recorded by the source corpus.
Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.
Subfields: Computational Theory and Mathematics, Organic Chemistry
View Paper: https://www.semanticscholar.org/paper/d7849967dbc68573d7be58a95db11c0ea4c5b08b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
Citations in the corpus
- OnionNet: a Multiple-Layer Intermolecular-Contact-Based Convolutional Neural Network for Protein–Ligand Binding Affinity Prediction (2019, background): “gand) are the only information considered. There are majorly a few advantages to use the distance-based contacts: (1) fewer features would be generated; (2) minimum bias or noise would be introduced; (3) large space around the ligand and both the local and non-local protein-ligand interactions would”