Blog
About

4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Deep learning for molecular design—a review of the state of the art

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We review a recent groundswell of work which uses deep learning techniques to generate and optimize molecules.

          Abstract

          In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text. Inspired by these successes, researchers are now applying deep generative modeling techniques to the generation and optimization of molecules—in our review we found 45 papers on the subject published in the past two years. These works point to a future where such systems will be used to generate lead molecules, greatly reducing resources spent downstream synthesizing and characterizing bad leads in the lab. In this review we survey the increasingly complex landscape of models and representation schemes that have been proposed. The four classes of techniques we describe are recursive neural networks, autoencoders, generative adversarial networks, and reinforcement learning. After first discussing some of the mathematical fundamentals of each technique, we draw high level connections and comparisons with other techniques and expose the pros and cons of each. Several important high level themes emerge as a result of this work, including the shift away from the SMILES string representation of molecules towards more sophisticated representations such as graph grammars and 3D representations, the importance of reward function design, the need for better standards for benchmarking and testing, and the benefits of adversarial training and reinforcement learning over maximum likelihood based training.

          Related collections

          Most cited references 70

          • Record: found
          • Abstract: not found
          • Article: not found

          Hybrid computing using a neural network with dynamic external memory

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Machine learning for molecular and materials science

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Automated design of ligands to polypharmacological profiles

              The clinical efficacy and safety of a drug is determined by its activity profile across multiple proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to rationally design drugs a priori against profiles of multiple proteins would have immense value in drug discovery. We describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein coupled receptors. Overall, 800 ligand-target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads where multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.
                Bookmark

                Author and article information

                Journal
                MSDEBG
                Molecular Systems Design & Engineering
                Mol. Syst. Des. Eng.
                Royal Society of Chemistry (RSC)
                2058-9689
                August 5 2019
                2019
                : 4
                : 4
                : 828-849
                Affiliations
                [1 ]Department of Mechanical Engineering
                [2 ]University of Maryland
                [3 ]Maryland
                [4 ]USA
                [5 ]Department of Mathematics and Statistics
                Article
                10.1039/C9ME00039A
                © 2019

                Free to read

                http://rsc.li/journals-terms-of-use#chorus

                Comments

                Comment on this article