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Showing 1–15 of 15 results
Advanced filters: Author: Alexander Tropsha Clear advanced filters

  • Finding optimal multi-drug combinations for pancreatic cancer remains a complex task. Here, authors across three different groups apply machine learning approaches to predict synergy across 1.6 million combinations of drugs for pancreatic cancer, 307 of which are validated experimentally.

    • Mohsen Pourmousa
    • Sankalp Jain
    • Alexey V. Zakharov
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-11

  • Advances with deep learning, the growth of databases of molecules for virtual screening and improvements in computational power have supported the emergence of a new field of quantitative structure–activity relationship (QSAR) modelling applications that Tropsha et al. term ‘deep QSAR’. This article discusses key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning, and the use of deep QSAR models in structure-based virtual screening.

    • Alexander Tropsha
    • Olexandr Isayev
    • Artem Cherkasov
    Reviews
    Nature Reviews Drug Discovery
    Volume: 23, P: 141-155

  • Publicly accessible databases are core resources for data-rich research, consolidating field-specific knowledge and highlighting best practices and challenges. Further effective growth of nanomaterial databases requires the concerted efforts of database stewards, researchers, funding agencies and publishers.

    • Alexander Tropsha
    • Karmann C. Mills
    • Anthony J. Hickey
    Comments & Opinion
    Nature Nanotechnology
    Volume: 12, P: 1111-1114

  • Phosphatidylinositol transfer proteins (PITPs) are important mediators of phosphoinositide signaling within cells. A small-molecule inhibitor of the PITP Sec14, identified by chemical screening and structure-based design, affects transit through the trans-Golgi network and endosomal system.

    • Aaron H Nile
    • Ashutosh Tripathi
    • Vytas A Bankaitis
    Research
    Nature Chemical Biology
    Volume: 10, P: 76-84

  • The increasing availability of data related to genes, proteins and their modulation by small molecules has provided a vast amount of biological information leading to the emergence of systems biology and the broad use of simulation tools for data analysis. However, there is a critical need to develop cheminformatics tools that can integrate chemical knowledge with these biological databases and simulation approaches, with the goal of creating systems chemical biology.

    • Tudor I Oprea
    • Alexander Tropsha
    • Mark D Rintoul
    Comments & Opinion
    Nature Chemical Biology
    Volume: 3, P: 447-450

  • Clozapine-induced agranulocytosis/granulocytopenia, or CIAG, is characterised by a rare and potentially fatal reaction to antipsychotic drugs. Here, the authors identify genetic variants in two immune-related genes that may contribute to the pathophysiology of CIAG.

    • Jacqueline I. Goldstein
    • L. Fredrik Jarskog
    • Patrick F. Sullivan
    Research
    Nature Communications
    Volume: 5, P: 1-9

  • Machine learning methods can be useful for materials discovery; however certain properties remain difficult to predict. Here, the authors present a universal machine learning approach for modelling the properties of inorganic crystals, which is validated for eight electronic and thermomechanical properties.

    • Olexandr Isayev
    • Corey Oses
    • Alexander Tropsha
    ResearchOpen Access
    Nature Communications
    Volume: 8, P: 1-12

  • Artificial intelligence is greatly accelerating research in drug discovery, but its development is still hindered by the lack of available data. Here the authors present data management and data science recommendations to help reach AI’s potential in the field.

    • Kristina Edfeldt
    • Aled M. Edwards
    • Matthieu Schapira
    ReviewsOpen Access
    Nature Communications
    Volume: 15, P: 1-10

  • Target 2035 aims to develop a potent and selective pharmacological modulator for every human protein by 2035 with the results made publicly available. This Roadmap article sets out how that will be achieved.

    • Aled M. Edwards
    • Dafydd R. Owen
    • Suzanne Ackloo
    Reviews
    Nature Reviews Chemistry
    Volume: 9, P: 634-645

  • GPUs, which are highly parallel computer processing units, were originally designed for graphics applications, but they have played an important role in accelerating the development of deep learning methods. In this Review, Pandey and colleagues summarize how GPUs have advanced machine learning in the field of drug discovery.

    • Mohit Pandey
    • Michael Fernandez
    • Artem Cherkasov
    Reviews
    Nature Machine Intelligence
    Volume: 4, P: 211-221

  • Deep generative neural networks are increasingly exploited for drug discovery, but often the majority of generated molecules are predicted to be inactive. Here, an optimized protocol for generative models with reinforcement learning is derived and applied to design potent epidermal growth factor inhibitors.

    • Maria Korshunova
    • Niles Huang
    • Olexandr Isayev
    ResearchOpen Access
    Communications Chemistry
    Volume: 5, P: 1-11