Steven M. Kearnes

Comparing massively-multitask regression algorithms for drug discovery

Eric J. Martin, Xiangwei Zhu, Patrick Riley, Steven M. Kearnes et autres

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Comparing Massively-Multitask Regression Algorithms for Drug Discovery

Éric Martin, Xiang‐Wei Zhu, Patrick Riley, Steven M. Kearnes et autres

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Comparing Massively-Multitask Regression Algorithms for Drug Discovery

Éric Martin, Xiang‐Wei Zhu, Patrick Riley, Steven M. Kearnes et autres

Massively-Multitask Regression Models (MMRMs) trained on millions of compounds and many thousands of assays can predict bioactivity with accuracy comparable to 4-concentration IC50 experiments. Recent advances in hardware and algorithms have produced a variety of methods for multitask modeling. This report compares …

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Comparing Massively-Multitask Regression Algorithms for Drug Discovery

Éric Martin, Xiang‐Wei Zhu, Patrick Riley, Steven M. Kearnes et autres

Massively-Multitask Regression Models (MMRMs) trained on millions of compounds and many thousands of assays can predict bioactivity with accuracy comparable to 4-concentration IC50 experiments. Recent advances in hardware and algorithms have produced a variety of methods for multitask modeling. This report compares …

us, hk, cn, gb (code pays fourni par la source)

Ordinal Confidence Level Assignments for Regression Model Predictions

Steven M. Kearnes, Patrick Riley

We present a simple method for assigning accurate confidence levels to molecular property predictions from regression models. These confidence levels are easy to interpret and useful for making decisions in drug discovery programs. We demonstrate their performance using time-split validation with assay …

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A Target Class Ligandability Evaluation of WD40 Repeat-Containing Proteins

Suzanne Z. Ackloo, Fengling Li, Magda Szewczyk, Almagul Seitova et autres

Target class-focused drug discovery has a strong track record in pharmaceutical research, yet public domain data indicate that many members of protein families remain unliganded. Here we present a systematic approach to scale up the discovery and characterization of small molecule ligands …

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A resource to enable chemical biology and drug discovery of WDR Proteins

Suzanne Z. Ackloo, Fengling Li, Magda Szewczyk, Almagul Seitova et autres

Abstract Protein class-focused drug discovery has a long and successful history in pharmaceutical research, yet most members of druggable protein families remain unliganded, often for practical reasons. Here we combined experiment and computation to enable discovery of ligands for WD40 repeat (WDR) …

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Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data

Rocío Mercado, Steven M. Kearnes, Connor W. Coley

The past decade has seen a number of impressive developments in predictive chemistry and reaction informatics driven by machine learning applications to computer-aided synthesis planning. While many of these developments have been made even with relatively small, bespoke data sets, in order …

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Defining Levels of Automated Chemical Design

Brian B. Goldman, Steven M. Kearnes, Trevor Joseph Kramer, Patrick Riley et autres

One application area of computational methods in drug discovery is the automated design of small molecules. Despite the large number of publications describing methods and their application in both retrospective and prospective studies, there is a lack of agreement on terminology and …

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Towards understanding retrosynthesis by energy-based models

Ruoxi Sun, Hanjun Dai, Li Li, Steven M. Kearnes et autres

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The Open Reaction Database

Steven M. Kearnes, Michael Maser, Michael Wleklinski, Anton Kast et autres

Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks are currently stored in various formats, often unstructured, which presents a significant barrier to downstream applications, including the training of machine-learning models. We present the Open Reaction Database (ORD), an …

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open-reaction-database/ord-data: v0.0.0

Steven M. Kearnes, Connor W. Coley

Initial release for alpha testing datasets before migrating to the updated schema in ord-schema>=0.2.0.

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