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  • Mail Code: 9025

All Publications

  • Feedback GAN for DNA optimizes protein functions NATURE MACHINE INTELLIGENCE Gupta, A., Zou, J. 2019; 1 (2): 105-111

    View details for DOI 10.1038/s42256-019-0017-4

    View details for Web of Science ID 000567066600008

  • Generative Recurrent Networks for De Novo Drug Design MOLECULAR INFORMATICS Gupta, A., Mueller, A. T., Huisman, B. H., Fuchs, J. A., Schneider, P., Schneider, G. 2018; 37 (1-2)

    Abstract

    Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets.

    View details for PubMedID 29095571

    View details for PubMedCentralID PMC5836943