Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses

Abstract

Bayesian models constructed from structure-derived fingerprints have been a popular and useful method for drug discovery research when applied to bioactivity measurements that can be effectively classified as active or inactive. The results can be used to rank candidate structures according to their probability of activity, and this ranking benefits from the high degree of interpretability when structure-based fingerprints are used, making the results chemically intuitive. Besides selecting an activity threshold, building a Bayesian model is fast and requires few or no parameters or user intervention. The method also does not suffer from such acute overtraining problems as quantitative structure–activity relationships or quantitative structure–property relationships (QSAR/QSPR). This makes it an approach highly suitable for automated workflows that are independent of user expertise or prior knowledge of the training data. We now describe a new method for creating a composite group of Bayesian models to extend the method to work with multiple states, rather than just binary. Incoming activities are divided into bins, each covering a mutually exclusive range of activities. For each of these bins, a Bayesian model is created to model whether or not the compound belongs in the bin. Analyzing putative molecules using the composite model involves making a prediction for each bin and examining the relative likelihood for each assignment, for example, highest value wins. The method has been evaluated on a collection of hundreds of data sets extracted from ChEMBL v20 and validated data sets for ADME/Tox and bioactivity.

Recent advances for identification of new scaffolds and drug targets for Mycobacterium tuberculosis

“…Some of these classification strategies include either traditional ones like clustering, partitioning, or machine learning techniques such as support vector machines, Bayesian models, and so on. . Both SBVS and LBVS methodologies require the actual or modeled structure and a comprehensive library of molecules spanning chemical space such as PubChem, ChemDB, Chemspider, ZINC, CHEMBL, and NCI.…”

Data Mining and Computational Modeling of High-Throughput Screening Datasets

“…This also shows how developing the open source technologies could benefit others outside of CDD and stimulate new technology development. More recently we have developed a Bayesian binning approach which is a step towards semi-quantitative Bayesian models 47 .…”

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

“…The open source descriptors and Bayesian algorithm have also been used outside of the CDD Vault to create several thousand models with the ChEMBL data, possibly representing the future of using thousands of models to score compounds simultaneously [83]. More recently, a Bayesian binning approach was developed that represents a move to semiquantitative Bayesian models [84]. Overall these combined efforts show how the open source technologies could benefit others and stimulate new technology applications in general.…”