A Deep Learning Approach to Antibiotic Discovery

Stokes, Jonathan; Yang, Kevin; Swanson, Kyle; Jin, Wengong; Cubillos-Ruiz, Andrés; Donghia, Nina M.; MacNair, Craig R.; French, Shawn; Carfrae, Lindsey A; Bloom-Ackermann, Zohar; Tran, Victoria M.; Chiappino-Pepe, Anush; Badran, Ahmed H.; Andrews, Ian W.; Chory, Emma J.; Church, George M.; Brown, Eric D.; Jaakkola, Tommi S.; Barzilay, Regina; Collins, James J.

doi:10.1016/j.cell.2020.01.021

Cited by 1,352 publications

(1,161 citation statements)

References 67 publications

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“…Specifically, we employed a deep learning-based model to effectively mine the complementary information in static clinical data and serial quantitative chest CT sequence. Since deep learning-based methods had been widely adopted and had achieved great performance in cancer outcome prediction 14 , head CT scans detection 15 and antibiotic discovery 16 . Moreover, compared with the traditional multi-stage methods, the deep learning-based model could significantly improve the efficiency of patient stratification (Figure 1), which is very important when dealing with tremendous patients.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2020

Preprint

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Background and purpose: The worldwide pandemic of coronavirus disease 2019 greatly challenges public medical systems. With limited medical resources, the treatment priority is determined by the severity of patients. However, many mild outpatients quickly deteriorate into severe/critical stage. It is crucial to early identify them and give timely treatment for optimizing treatment strategy and reducing mortality. This study aims to establish an AI model to predict mild patients with potential malignant progression.Methods: A total of 133 consecutively mild COVID-19 patients at admission who was hospitalized in Wuhan Pulmonary Hospital from January 3 to February 13, 2020, were selected in this retrospective IRB-approved study. All mild patients were categorized into groups with or without malignant progression. The clinical and laboratory data at admission, the first CT, and the follow-up CT at the severe/critical stage of the two groups were compared. Both multivariate logistic regression and deep learning-based methods were used to build the prediction models, with their area under ROC curves (AUC) compared.Results: Multivariate logistic regression depicted 6 risk factors for malignant progression: age >55years (OR 5.334, 95%CI 1.8-15.803), comorbid with hypertension (OR 5.093, 95%CI 1.236-20.986), a decrease of albumin (OR 4.01, 95%CI 1.216-13.223), a decrease of lymphocyte (OR 3.459, 95%CI 1.067-11.209), the progressive consolidation from CT1 to CTsevere (OR 1.235, 95%CI 1.018-1.498), and elevated HCRP (OR 1.015, 95%CI 1.002-1.029); and one protective factor: the presence of fibrosis at CT1 (OR 0.656, 95%CI 0.473-0.91). By combining the clinical data and the temporal information of the CT data, our deep learning-based models achieved the best AUC of 0.954, which outperformed logistic regression (AUC: 0.893), Conclusions: Our deep learning-based methods can identify the mild patients who are easy to deteriorate into severe/critical cases efficiently and accurately, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.

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Section: Introductionmentioning

confidence: 99%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2020

Preprint

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“…Recently, uses for DL have been described in health care, particularly in screening for breast cancer 17,18 and for use with electrocardiogram (ECG) traces 19,20 . Potential novel antibiotics were searched out by screening known drug databases for structures 21 . The strength of this approach lies in that these drugs already have significant results and may have clinical trial data.…”

Section: Current and Upcoming Uses For DL In Medicine And Health Carementioning

confidence: 99%

Leveraging Deep Learning to Simulate Coronavirus Spike proteins has the potential to predict future Zoonotic sequences

Crossman

2020

Preprint

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“…(1,2) For example, advancements in small molecule representation and generation have permitted de novo discovery of kinase inhibitors and identification of new antibiotics in a commercial database. (3,4) Prediction of peptide vaccines also benefitted from machine learning. (5) However, in all of these cases, the models use binary classifiers to predict candidates as a "hit" or "not a hit".…”

Section: Main Textmentioning

confidence: 99%

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

Schissel

Mohapatra

Wolfe

et al. 2020

Preprint

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There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we couple supervised and unsupervised deep learning with highthroughput experimentation to drive the design of high-activity, novel sequences reaching 10 kDa that deliver antisense oligonucleotides to the nucleus of cells. The models, in which natural and unnatural residues are represented as topological fingerprints, decipher and visualize sequenceactivity predictions. The new variants boost antisense activity by 50-fold, are effective in animals, are nontoxic, and can also deliver proteins into the cytosol. Machine learning can discover functional polymers that enhance cellular uptake of biotherapeutics, with significant implications toward developing therapies for currently untreatable diseases.One sentence summary: Deep learning generates de novo large functional abiotic polymers that deliver antisense oligonucleotides to the nucleus. typically occurs by energy-dependent uptake, meaning that endosomal escape presents an additional challenge. While some peptides can efficiently escape the endosome, designing a novel CPP sequence for this task is nearly impossible. In addition to the diversity of physicochemical properties of CPPs, variation in experimental design has resulted in inconsistent and sometimes contradictory datasets.(22) These inconsistencies preclude establishing sequence-activity relationships to guide the design of next-generation CPPs and can be remedied by testing PMO-CPP conjugates in a nuclear delivery-based assay that provides quantitative activity data and selects for sequences that can escape the endosome. In order to uncover CPP design principles for PMO delivery, it is necessary to have a standardized, biologically relevant dataset with which to train machine learning models.

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A Deep Learning Approach to Antibiotic Discovery

Cited by 1,352 publications

References 67 publications

Deep learning for predicting COVID-19 malignant progression

Deep learning for predicting COVID-19 malignant progression

Leveraging Deep Learning to Simulate Coronavirus Spike proteins has the potential to predict future Zoonotic sequences

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

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