Skip to main content
OSTI.GOVU.S. Department of Energy Office of Scientific and Technical Information
U.S. Department of Energy
Office of Scientific and Technical Information
 

Transforming Computational Drug Discovery with Machine Learning and AI

Journal Article · · ACS Medicinal Chemistry Letters
 [1];  [2];  [3]
  1. Univ. of Florida, Gainesville, FL (United States). Dept. of Chemistry; Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of Florida, Gainesville, FL (United States). Dept. of Chemistry
  3. Univ. of North Carolina, Chapel Hill, NC (United States). UNC Eshelman School of Pharmacy
+ Show Author Affiliations

We discuss the current progress in applications of machine learning (ML) and artificial intelligence (AI) to meet the challenges of computational drug discovery. We identify several areas where existing methods have the potential to accelerate pharmaceutical research and disrupt more traditional approaches.

Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
USDOE; LANL Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF) (United States); Office of Naval Research (ONR) (United States)
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1483548
Report Number(s):
LA-UR-18-28962
Journal Information:
ACS Medicinal Chemistry Letters, Journal Name: ACS Medicinal Chemistry Letters Journal Issue: 11 Vol. 9; ISSN 1948-5875
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English

References (11)

Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory journal March 2018
A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Druglike Fragments journal May 2017
Planning chemical syntheses with deep neural networks and symbolic AI journal March 2018
Controlling an organic synthesis robot with machine learning to search for new reactivity journal July 2018
Machine learning for molecular and materials science journal July 2018
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules journal December 2017
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost journal January 2017
Machine learning molecular dynamics for the simulation of infrared spectra journal January 2017
Less is more: Sampling chemical space with active learning journal June 2018
Deep reinforcement learning for de novo drug design journal July 2018
Inverse molecular design using machine learning: Generative models for matter engineering journal July 2018

Cited By (4)

IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads conference October 2021
Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns journal November 2019
Machine learning and artificial neural network accelerated computational discoveries in materials science journal November 2019
Applications of machine learning in drug discovery and development journal April 2019

Figures / Tables (2)

Figure 1(p. 5)figure Figure 1
Figure 2(p. 6)figure Figure 2

Similar Records

Transforming Energy Through Computational Excellence: Artificial Intelligence and Machine Learning
Program Document · Sun Jan 30 19:00:00 EST 2022 · OSTI ID:1843834
Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation
Journal Article · Tue Mar 22 00:00:00 EDT 2022 · Frontiers in Materials · OSTI ID:1858044

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
60 APPLIED LIFE SCIENCES
97 MATHEMATICS AND COMPUTING
medicinal chemistry
drug discovery
machine learning
chemistry