Title: Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery

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 ATOM Modeling Pipeline (AMPL) has been developed to enable advanced and emerging machine learning (ML) approaches to build models from diverse historical drug discovery data. This modular pipeline has been designed 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, we discuss the role of collaborative efforts such as consortia and public-private partnerships in accelerating cross disciplinary innovation and the development of opensource tools for drug discovery.

Authors:

Hinkson, Izumi V.; Madej, Benjamin; Stahlberg, Eric A.

Publication Date:

Tue Jun 30 00:00:00 EDT 2020

Research Org.:

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1635417

Alternate Identifier(s):

OSTI ID: 1817145

Grant/Contract Number:  

AC52-07NA27344

Resource Type:

Published Article

Journal Name:

Frontiers in Pharmacology

Additional Journal Information:

Journal Name: Frontiers in Pharmacology Journal Volume: 11; Journal ID: ISSN 1663-9812

Publisher:

Frontiers Media SA

Country of Publication:

Switzerland

Language:

English

Subject:

60 APPLIED LIFE SCIENCES; artificial intelligence; machine learning; drug discovery and development; data science; in silico modeling