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Showing 1–21 of 21 results
Advanced filters: Author: Connor W. Coley Clear advanced filters

  • The downselection of compounds for synthesis is a key challenge in molecular design cycles that typically relies on expert chemist intuition. Fromer and Coley propose a cost-aware method to automatically select compounds and synthetic routes.

    • Jenna C. Fromer
    • Connor W. Coley
    Research
    Nature Computational Science
    Volume: 4, P: 440-450

  • Native proteins use hydration frustration—regulating the dehydration of hydrophilic residues and the hydration of hydrophobic residues—to enhance their activity. Now it has been shown that single-polymer-chain nanoparticles made from random heteropolymers can exhibit similar hydration frustration, following design rules orthogonal to those of proteins.

    • Tianyi Jin
    • Connor W. Coley
    • Alfredo Alexander-Katz
    Research
    Nature Chemistry
    Volume: 17, P: 997-1004

  • A new tool based on generative machine learning called FlowER uses flow matching to model reactions as the redistribution of electrons between reactants and products, enabling the enforcement of mass conservation in reaction prediction.

    • Joonyoung F. Joung
    • Mun Hong Fong
    • Connor W. Coley
    Research
    Nature
    Volume: 645, P: 115-123

  • Tandem mass spectroscopy is a useful tool to identify metabolites but is limited by the capability of computational methods to annotate peaks with chemical structures when spectra are dissimilar to previously observed spectra. Goldman and colleagues use a transformer-based method to annotate chemical structure fragments, thereby incorporating domain insights into its architecture, and to simultaneously predict the structure of the metabolite and its fragments from the spectrum.

    • Samuel Goldman
    • Jeremy Wohlwend
    • Connor W. Coley
    Research
    Nature Machine Intelligence
    Volume: 5, P: 965-979

  • Achieving autonomous multi-step synthesis of novel molecular structures in chemical discovery processes is a goal shared by many researchers. In this Comment, we discuss key considerations of what an ideal platform may look like and the apparent state of the art. While most hardware challenges can be overcome with clever engineering, other challenges will require advances in both algorithms and data curation.

    • Wenhao Gao
    • Priyanka Raghavan
    • Connor W. Coley
    Comments & OpinionOpen Access
    Nature Communications
    Volume: 13, P: 1-4

  • Deep learning methods in natural language processing generally become more effective with larger datasets and bigger networks. But it is not evident whether the same is true for more specialized domains such as cheminformatics. Frey and colleagues provide empirical explorations of chemistry models and find that neural-scaling laws hold true even for the largest tested models and datasets.

    • Nathan C. Frey
    • Ryan Soklaski
    • Vijay Gadepally
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 5, P: 1297-1305

  • Most DNA-encoded library (DEL) syntheses are limited by the presence of sensitive DNA-based constructs. Here, the authors develop DOSEDO, a diverse 3.7 million compound DEL, generated through diversity-oriented synthesis that provides enhanced scaffold and exit vector diversity and gives validated binding hits for multiple protein targets.

    • Liam Hudson
    • Jeremy W. Mason
    • Karin Briner
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-15

  • High-throughput synthesis of polypeptides through ring-opening polymerization of N-carboxyanhydride is challenging. Now a diversification approach is developed based on the post-polymerization modification of a selenium-containing polypeptide. With the assistance of automation and model-guided optimization, this approach enables the discovery of functional polypeptides from chemical space with little previous knowledge.

    • Guangqi Wu
    • Haisen Zhou
    • Hua Lu
    Research
    Nature Synthesis
    Volume: 2, P: 515-526

  • The identification of synthetic routes combining enzymatic and non-enzymatic reactions has been challenging and requiring expert knowledge. Here, the authors describe a computational retrosynthetic approach relying on neural network models for planning synthetic routes using both strategies.

    • Itai Levin
    • Mengjie Liu
    • Connor W. Coley
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-14

  • Cutting-edge chemistry is often performed in non-atmospheric conditions. Continued development of the Chemputer platform now enables the utilization of sensitive compounds in automated synthetic protocols.

    • Babak A. Mahjour
    • Connor W. Coley
    News & Views
    Nature Reviews Chemistry
    Volume: 8, P: 300-301

  • As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its progress. Despite hidden variables and ‘unknown unknowns’ in datasets that may impede the realization of a digital twin for the laboratory flask, there are many opportunities to leverage AI and large datasets to advance synthesis science.

    • Nicholas David
    • Wenhao Sun
    • Connor W. Coley
    Comments & Opinion
    Nature Computational Science
    Volume: 3, P: 362-364

  • Artificial intelligence (AI) is poised to transform therapeutic science. Therapeutics Data Commons is an initiative to access and evaluate AI capability across therapeutic modalities and stages of discovery, establishing a foundation for understanding which AI methods are most suitable and why.

    • Kexin Huang
    • Tianfan Fu
    • Marinka Zitnik
    Comments & Opinion
    Nature Chemical Biology
    Volume: 18, P: 1033-1036

  • The advances in artificial intelligence over the past decade are examined, with a discussion on how artificial intelligence systems can aid the scientific process and the central issues that remain despite advances.

    • Hanchen Wang
    • Tianfan Fu
    • Marinka Zitnik
    Reviews
    Nature
    Volume: 620, P: 47-60