ArticleJournal of computer-aided molecular design2015
A probabilistic method to report predictions from a human liver microsomes stability QSAR model: a practical tool for drug discovery.
Article in Journal of computer-aided molecular design, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 26 citations in OpenAlex.
- Assessing the impact of data harmonization on human liver microsomal stability prediction model performance.Results in chemistry · 2026Article
- Developing Robust Human Liver Microsomal Stability Prediction Models: Leveraging Inter-Species Correlation with Rat Data.Pharmaceutics · 2024Article
- Machine Learning ADME Models in Practice: Four Guidelines from a Successful Lead Optimization Case Study.ACS medicinal chemistry letters · 2024Article
- Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction.Journal of computer-aided molecular design · 2022Article
- Article
- A review on machine learning approaches and trends in drug discovery.Computational and structural biotechnology journal · 2021Review
- Advances in the study of drug metabolism - symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX).Drug metabolism reviews · 2020Review
- chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery.Journal of cheminformatics · 2017Article
- An integrated suite of modeling tools that empower scientists in structure- and property-based drug design.Journal of computer-aided molecular design · 2015Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using data from the in vitro liver microsomes metabolic stability assay, we have developed QSAR models to predict in vitro human clearance. Models were trained using in house high-throughput assay data reported as the predicted human hepatic clearance by liver microsomes or pCLh. Machine learning regression methods were used to generate the models. Model output for a given molecule was reported as its probability of being metabolically stable, thus allowing for synthesis prioritization based on this prediction. Use of probability, instead of the regression value or categories, has been found to be an efficient way for both reporting and assessing predictions. Model performance is evaluated using prospective validation. These models have been integrated into a number of desktop tools, and the models are routinely used to prioritize the synthesis of compounds. We discuss two therapeutic projects at Genentech that exemplify the benefits of a probabilistic approach in applying the models. A three-year retrospective analysis of measured liver microsomes stability data on all registered compounds at Genentech reveals that the use of these models has resulted in an improved metabolic stability profile of synthesized compounds.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.