A Deep Learning Approach to Antibiotic Discovery
Jonathan M. Stokes,Kevin Yang,Kyle Swanson,Wengong Jin,Andres Cubillos-Ruiz,Nina M. Donghia,Craig R. MacNair,Shawn French,Lindsey A. Carfrae,Zohar Bloom-Ackermann,Victoria M. Tran,Anush Chiappino-Pepe,Ahmed H. Badran,Ian W. Andrews,Ian W. Andrews,Ian W. Andrews,Emma J. Chory,George M. Church,Eric D. Brown,Tommi S. Jaakkola,Regina Barzilay,James J. Collins +21 more
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TLDR
A deep neural network capable of predicting molecules with antibacterial activity is trained and a molecule from the Drug Repurposing Hub-halicin- is discovered that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens.About:
This article is published in Cell.The article was published on 2020-02-20 and is currently open access. It has received 1002 citations till now.read more
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Evolving Computation Graphs
TL;DR: In this article , the authors propose an evolving computation graph (ECG) method for enhancing GNNs on heterophilic datasets by rewiring the computation graph towards adding edges that connect nodes that are likely to be in the same class.
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Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery
TL;DR: The authors examined the potential of large language models (LLMs) for dialoguing with biomedical background knowledge, using the context of antibiotic discovery as an exemplar motivational scenario and provided a systematic assessment on the ability of LLMs to encode and express these relations, verifying for fluency, prompt alignment, semantic coherence, factual knowledge and specificity of generated responses.
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A Review on Deep Learning-Driven Drug Discovery: Strategies, Tools and Applications.
TL;DR: Deep learning (DL) holds great potential for drug development since it allows for sophisticated image interpretation, molecular structure and function prediction, and the automated creation of novel chemical entities with specific features as discussed by the authors .
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