Principles of early drug discovery

By J. Hughes, S. Rees, SB Kalindjian, S. Kalindjian, KL Philpott. 2011

2,463 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Physical and Theoretical Chemistry, Computational Chemistry & Drug Discovery

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In Computational Theory and Mathematics

In Molecular Biology

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, neutral): “The research-based pharmaceutical industry has increasingly employed modern medicinal chemistry methods, including molecular modeling, as powerful tools for the study of structure-activity relationships (SAR) [1].”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “…across different scaffolds Drug discovery is hugely expensive and time-consuming: the entire process typically takes more than 10 years, during which many expensive wet lab experiments need to be performed to find a potent molecule (Hughes et al., 2011), before clinical trials can even start.”
Low-N protein engineering with data-efficient deep learning (2020, background): “Consequently, the best candidates identified by early stage high-throughput (>10 4 variants) proxy experiments 9,11,14 will often fail in validation under higher-fidelity, later stage assays 13,15–17 .”
The rise of artificial intelligence in healthcare applications (2020, background): “This makes the drug innovation process both challenging and inefficient with a high price tag on any new drug products that make it onto the market [14].”
Parallelised Bayesian Optimisation via Thompson Sampling (2018, background): “For example, in pre-clinical drug discovery, high throughput screening equipment can test a few thousand compounds in parallel, but only in batches [16].”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, methodology): “Traditional method to gauge the affinities are through high-throughput screening wet-lab experiments (Hughes et al. 2011).”
Potential of quantum computing for drug discovery (2018, background): “The identiﬁcation of suitable targets and their corresponding validation by studies of the mechanism of action increases the chance of success during the discovery process and allows one to foresee side effects associated to the modulation of the target [12].”
Artificial intelligence in drug development: present status and future prospects. (2019, background): “DL has demonstrated outstanding success in proposing potent drug candidates and accurately predicting their properties and the possible toxicity risks [39].”
Utilizing graph machine learning within drug discovery and development (2021, background): “The early discovery stage involves target identification and validation, hit discovery, lead molecule identification and then optimization to achieve the desired characteristics of a drug candidate [114].”