Drug discovery is the task of applying machine learning to discover new candidate drugs.
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While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.
The necessity of predictive models in the drug discovery industry cannot be understated.
In machine learning approaches, the numerical representation of molecules is vital to the performance of the model.
Drug target interaction (DTI) prediction is a foundational task for in silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space.
Uncompetitive antagonists of the N-methyl D-aspartate receptor (NMDAR) have demonstrated therapeutic benefit in the treatment of neurological diseases such as Parkinson's and Alzheimer's, but some also cause dissociative effects that have led to the synthesis of illicit drugs.
This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences.
Naloxone, an opioid antagonist, has been widely used to save lives from opioid overdose, a leading cause for death in the opioid epidemic.
Understanding the physicochemical properties of ligand-binding sites is very important in the field of drug discovery as well as understanding biological systems.
Building in silico models to predict chemical properties and activities is a crucial step in drug discovery.
Deep learning has dramatically improved the performance in many application areas such as image classification, object detection, speech recognition, drug discovery and etc since 2012.