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Publications
TITLE:
Machine Learning Models to Predict Inhibition of the Bile Salt Export Pump , Journal of Chemical Information and Modeling , 2 , 61 : 587-602 , 2021
PUBLICATION DATE: 02-22-2021
ABSTRACT: Cholestatic liver injury is frequently associated with drug inhibition of bile salt transporters, such as the bile salt export pump (BSEP). Reliable in silico models to predict BSEP inhibition directly from chemical structures would significantly reduce costs during drug discovery and could help avoid injury to patients. The authors reported their development of classification and regression models for BSEP inhibition with substantially improved performance over previously published models. The authors assessed the performance effects of different methods of chemical featurization, data set partitioning, and class labeling and identified the methods producing models that generalized best to novel chemical entities.
PROJECT: ATOM
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TITLE:
Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery , Frontiers in Pharmacology , 11 , 2020
PUBLICATION DATE: 06-30-2020
ABSTRACT: Conventional drug discovery is long and costly, and suffers from high attrition rates, often leaving patients with limited or expensive treatment options. Recognizing the overwhelming need to accelerate this process and increase success, the ATOM consortium was formed by government, industry, and academic partners in October 2017. ATOM applies a team science and open-source approach to foster a paradigm shift in drug discovery. ATOM is developing and validating a precompetitive, preclinical, small molecule drug discovery platform that simultaneously optimizes pharmacokinetics, toxicity, protein-ligand interactions, systems-level models, molecular design, and novel compound generation. To achieve this, the authors of this article developed the ATOM Modeling PipeLine (AMPL) to enable advanced and emerging machine learning (ML) approaches to build models from diverse historical drug discovery data. The authors designed this modular pipeline to couple with a generative algorithm that optimizes multiple parameters necessary for drug discovery. ATOM's approach is to consider the full pharmacology and therapeutic window of the drug concurrently, through computationally driven design, thereby reducing the number of molecules that are selected for experimental validation. Here, the authors discussed the role of collaborative efforts such as consortia and public-private partnerships in accelerating cross disciplinary innovation and the development of open-source tools for drug discovery.
PROJECT: ATOM
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TITLE:
AMPL: A Data-Driven Modeling PipeLine for Drug Discovery , Journal of Chemical Information and Modeling , 4 , 60 : 1955-1968 , 2020
PUBLICATION DATE: 04-27-2020
ABSTRACT: One of the key requirements for incorporating machine learning (ML) into the drug discovery process is complete traceability and reproducibility of the model building and evaluation process. With this in mind, the authors have developed an end-to-end modular and extensible software pipeline for building and sharing ML models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of ML and molecular featurization tools. The authors have benchmarked AMPL on a large collection of pharmaceutical data sets covering a wide range of parameters. Their key findings indicated that traditional molecular fingerprints underperform other feature representation methods. The authors also found that data set size correlates directly with prediction performance, which points to the need to expand public data sets. Uncertainty quantification can help predict model error, but correlation with error varies considerably between data sets and model types. Their findings point to the need for an extensible pipeline that the community can share to make model building more widely accessible and reproducible. This software is open source and available at: https://github.com/ATOMconsortium/AMPL.
PROJECT: ATOM
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