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Finton Sirockin

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Articles

Scaffold Hopping with Generative Reinforcement Learning

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By Finton Sirockin, Nadine Schneider, Francesca Grisoni, Luke Rossen
| dx.doi.org
Special Issue Published as part of Journal of Chemical Information and Modeling special issue “Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning”. Introduction Click to copy section linkSection link copied! In recent years, generative deep learning has shown its promise for de novo drug design. (1−6) Generative approaches can explore the chemical space and produce molecules with desirable properties on-demand.
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Balancing Molecular Size, Activity, Permeability, and Other Properties: Drug Candidates in the Context of Their Chemical Structure Optimization

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By Maximilian Beckers, Finton Sirockin, Nikolas Fechner, Nikolaus Stiefl
| dx.doi.org
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Prediction of Small-Molecule Developability Using Large-Scale In Silico ADMET Models

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By Maximilian Beckers, Noé Sturm, Finton Sirockin, Nikolas Fechner
| dx.doi.org
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Med. Chem. 2023, XXXX, XXX Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses).
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