Title |
A D3R prospective evaluation of machine learning for protein-ligand scoring
|
---|---|
Published in |
Perspectives in Drug Discovery and Design, September 2016
|
DOI | 10.1007/s10822-016-9960-x |
Pubmed ID | |
Authors |
Jocelyn Sunseri, Matthew Ragoza, Jasmine Collins, David Ryan Koes |
Abstract |
We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to evaluate the utility of various strategies for training set curation and binding pose generation, but they share a novel approach to classification in the context of protein-ligand scoring. Rather than explicitly using structural data such as affinity values or information extracted from crystal binding poses for training, we instead exploit the abundance of data available from high-throughput screening to approach the problem as one of discriminating binders from non-binders. We evaluate the performance of our various scoring methods in the 2015 D3R Grand Challenge and find that although the merits of some features of our approach remain inconclusive, our scoring methods performed comparably to a state-of-the-art scoring function that was fit to binding affinity data. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 20% |
Belgium | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
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Scientists | 3 | 60% |
Members of the public | 2 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 50 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 13 | 26% |
Student > Ph. D. Student | 8 | 16% |
Student > Bachelor | 4 | 8% |
Professor > Associate Professor | 4 | 8% |
Student > Master | 4 | 8% |
Other | 6 | 12% |
Unknown | 11 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 8 | 16% |
Computer Science | 8 | 16% |
Biochemistry, Genetics and Molecular Biology | 6 | 12% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 6% |
Agricultural and Biological Sciences | 3 | 6% |
Other | 8 | 16% |
Unknown | 14 | 28% |