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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Leveraging Deep Learning to Simulate Coronavirus Spike proteins has the potential to predict future Zoonotic sequences
TL;DR: Deep learning recurrent neural networks are trained to produce simulated spike protein sequences, which may be able to aid in knowledge and/or vaccine design by creating alternative possible spike sequences that could arise from zoonotic sources in future.
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Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review
Benjamin Kompa,Joe B. Hakim,Anil K. Palepu,Kathryn Grace Kompa,Michael Smith,Paul A. Bain,Stephen Woloszynek,Jeffery L. Painter,Andrew Bate,Andrew L. Beam +9 more
TL;DR: In this paper , the authors conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacoviggerance through the application of machine learning.
Journal ArticleDOI
Unconventional Antibacterials and Adjuvants.
TL;DR: The need for new classes of antibacterials and adjuvants is genuine in light of the dearth of clinical options for the treatment of bacterial infections as mentioned in this paper, which has led to a renewed interest in old drugs, the repurposing of the existing antibiotics and pairing of synergistic antibiotics or of an antibiotic with an adjuvant.
Journal ArticleDOI
GEOM, energy-annotated molecular conformations for property prediction and molecular generation
TL;DR: The Geometric Ensemble Of Molecules (GEOM) dataset as mentioned in this paper contains conformers for 133,000 species from QM9 and 317, 000 species with experimental data related to biophysics, physiology, and physical chemistry.
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RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design.
Cheng-Hao Liu,Maksym Korablyov,Stanisław Jastrzębski,Paweł Włodarczyk-Pruszyński,Yoshua Bengio,Marwin H. S. Segler +5 more
TL;DR: This work investigates the feasibility of training deep graph neural networks to approximate the outputs of a retrosynthesis planning software, and their use to bias the search process, and finds molecules predicted to be more likely to be antibiotics while maintaining good drug-like properties and being easily synthesizable.
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