Evidential Deep Learning for Guided Molecular Property Prediction and Discovery-Reference-Cited by-同舟云学术

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery

Published:2021-07-27 Issue:8 Volume:7 Page:1356-1367
ISSN:2374-7943
Container-title:ACS Central Science
language:en
Short-container-title:ACS Cent. Sci.

Author:

Soleimany Ava P.¹²³^ORCID,Amini Alexander⁴,Goldman Samuel⁵,Rus Daniela⁴,Bhatia Sangeeta N.¹⁴⁶^ORCID,Coley Connor W.⁷^ORCID

Affiliation:

1. Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, Massachusetts 02139, United States

2. Graduate Program in Biophysics, Harvard University, Boston, Massachusetts 02115, United States

3. Microsoft Research New England, Cambridge, Massachusetts 02142, United States

4. Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts 02139, United States

5. Computational and Systems Biology, MIT, Cambridge, Massachusetts 02139, United States

6. Howard Hughes Medical Institute, Cambridge, Massachusetts 02139, United States

7. Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States

Funder

Howard Hughes Medical Institute

National Institute of General Medical Sciences

National Science Foundation

Machine Learning for Pharmaceutical Discovery and Synthesis Consortium

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acscentsci.1c00546

Reference55 articles.

1. Nigam, A.; Pollice, R.; Hurley, M. F.; Hickman, R. J.; Aldeghi, M.; Yoshikawa, N.; Chithrananda, S.; Voelz, V. A.; Aspuru-Guzik, A. Assigning Confidence to Molecular Property Prediction. 2021, arXiv preprint arXiv:2102.11439. https://arxiv.org/abs/2102.11439 (accessed 2021-07-19).

2. Lamb, G.; Paige, B. Bayesian Graph Neural Networks for Molecular Property Prediction. 2020, arXiv preprint arXiv:2012.02089. https://arxiv.org/abs/2012.02089 (accessed 2021-07-19).

3. Drug discovery with explainable artificial intelligence

4. QSAR Modeling: Where Have You Been? Where Are You Going To?

5. QSAR without borders

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