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Anvita Gupta
Researcher at Stanford University
Publications - 5
Citations - 546
Anvita Gupta is an academic researcher from Stanford University. The author has contributed to research in topics: Synthetic biology & Deep learning. The author has an hindex of 4, co-authored 5 publications receiving 353 citations. Previous affiliations of Anvita Gupta include École Polytechnique Fédérale de Lausanne.
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Journal ArticleDOI
Generative Recurrent Networks for De Novo Drug Design.
Anvita Gupta,Anvita Gupta,Alex T. Müller,Berend J. H. Huisman,Jens A. Fuchs,Petra Schneider,Gisbert Schneider +6 more
TL;DR: This paper presents a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short‐term memory (LSTM) cells that captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy.
Journal ArticleDOI
Feedback GAN for DNA optimizes protein functions
Anvita Gupta,James Zou +1 more
TL;DR: A novel feedback-loop architecture is proposed, feedback GAN (FBGAN), to optimize the synthetic gene sequences for desired properties using an external function analyser, and it is demonstrated that the GAN-generated proteins have desirable biophysical properties.
Posted Content
Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions.
Anvita Gupta,James Zou +1 more
TL;DR: A novel feedback-loop architecture is proposed, called Feedback GAN (FBGAN), to optimize the synthetic gene sequences for desired properties using an external function analyzer, and can be used to optimize GAN-generated datapoints for useful properties in domains beyond genomics.
Journal ArticleDOI
Erratum: Generative Recurrent Networks for De Novo Drug Design.
Anvita Gupta,Alex T. Müller,Berend J. H. Huisman,Jens A. Fuchs,Petra Schneider,Gisbert Schneider +5 more
Posted ContentDOI
Targeted optimization of regulatory DNA sequences with neural editing architectures
Anvita Gupta,Anshul Kundaje +1 more
TL;DR: A novel generative neural network architecture for targeted DNA sequence editing – the EDA architecture – consisting of an encoder, decoder, and analyzer is proposed and significantly improves predicted binding of SPI1 of genomic sequences with the minimal set of edits.