Adversarial Threshold Neural Computer for Molecular de Novo Design
Evgeny Putin,Arip Asadulaev,Quentin Vanhaelen,Yan A. Ivanenkov,Yan A. Ivanenkov,Anastasia V. Aladinskaya,Alexander Aliper,Alex Zhavoronkov +7 more
TLDR
Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties, indicating that ATNC is an effective method for producing hit compounds.Abstract:
In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp3-rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection...read more
Citations
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Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
Alex Zhavoronkov,Yan A. Ivanenkov,Alexander Aliper,Mark S. Veselov,Vladimir A. Aladinskiy,Anastasiya V Aladinskaya,Victor A Terentiev,Daniil Polykovskiy,Maksim Kuznetsov,Arip Asadulaev,Yury Volkov,Artem Zholus,Rim Shayakhmetov,Alexander Zhebrak,Lidiya I Minaeva,Bogdan A Zagribelnyy,Lennart H Lee,Richard Soll,David Madge,Li Xing,Tao Guo,Alán Aspuru-Guzik +21 more
TL;DR: A machine learning model allows the identification of new small-molecule kinase inhibitors in days and is used to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days.
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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
TL;DR: The current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects.
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Deep learning for molecular design—a review of the state of the art
TL;DR: A survey of deep generative modeling techniques for the optimization of molecules can be found in this article, where four classes of techniques are described: recursive neural networks, autoencoders, generative adversarial networks, and reinforcement learning.
Journal ArticleDOI
Exploiting machine learning for end-to-end drug discovery and development
Sean Ekins,Ana C. Puhl,Kimberley M. Zorn,Thomas J. Lane,Daniel P. Russo,Jennifer J. Klein,Anthony J. Hickey,Anthony J. Hickey,Alex M. Clark +8 more
TL;DR: The application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process has considerable implications for developing future therapies and their targeting.
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Optimization of Molecules via Deep Reinforcement Learning
TL;DR: Molecule Deep Q-Networks (MolDQN) as mentioned in this paper combines double Q-learning and randomized value functions for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques.
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