Transfer Learning for Drug Discovery

Cai, Chenjing; Wang, Shiwei; Xu, Youjun; Zhang, Weilin; Tang, Ke; Ouyang, Qi; Lai, Luhua; Pei, Jianfeng

doi:10.1021/acs.jmedchem.9b02147

Cited by 266 publications

(209 citation statements)

References 84 publications

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“…Transfer learning, an important tool in AI, can be utilized to surmount the restriction of limited amounts of data. [29][30][31][32] With transfer learning, the knowledge of solving one task can be applied to another task. For example, general chemical knowledge from the former large chemical dataset can be applied to the latter relative but different reaction prediction task with limited labeled data.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

et al. 2021

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“…Transfer learning (TL) is an AI technology that can be applied to resolve problems of data scarcity by leveraging existing knowledge from other related tasks to a specific task with low data 31 . Transfer learning have achieved success on low data tasks in many fields including computer vision 32 , natural language processing 33,34 and drug discovery 35,36 . In the present study, we implemented fine-tuning technique, which is one of the most commonly used transfer learning techniques, to deal with the data scarcity problem for anti-SARS-CoV-2 prediction model.…”

Section: Methodsmentioning

confidence: 99%

A Transferable Deep Learning Approach to Fast Screen Potent Antiviral Drugs against SARS-CoV-2

Wang

Sun

et al. 2020

Preprint

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The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. We used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds. The fine-tuned model was applied to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested 7 high-scored compounds that also demonstrated good binding strength in docking study against the 3C-like protease of SARS-CoV-2 and found one novel compound that inhibited the enzyme with an IC50 of 37.0 μM. Our model is highly efficient and can be used to screen large compound databases with billions or more compounds to accelerate the drug discovery process for the treatment of COVID-19.

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“…There are four types of learning which includes instance based, feature based, parameter based, and relation based (shown in Table 1). Combining different machine learning methods can give better results (6).…”

Section: Transfer Learningmentioning

confidence: 99%

Transfer Learning-Based Classification of Breast Cancer using Ultrasound Images

Güldoğan

Ucuzal

Küçükakçali

et al. 2021

Middle Black Sea Journal of Health Science

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Objective: One of the most significant cancers impacting the health of women is breast cancer. This study aimed to provide breast cancer classification (benign and malignant) using the transfer learning method on the ultrasound images. Methods: In the present study, a public imaging dataset was used for the breast cancer classification. Transfer learning technique was implemented for the detection and classification of breast cancer (benign or malignant) based on the ultrasound images. The current research includes data of 150 cases of malignant and 100 normal cases obtained from the Mendeley data. The relevant dataset was partitioned into training (85% of the images) and validation (15% of the images) sets. The present study implemented Teachable Machine (teachablemachine.withgoogle.com) for predicting the benign or malignant of breast cancer tumor based on the ultrasound images. Results: According to the experimental results, accuracy, sensitivity and specificity with 95% confidence intervals were 0.974 (0.923-1.0), 0.957 (0.781-0.999) and 1 (0.782-1.0), respectively. Conclusion:The model proposed in this study gave predictions that could be useful to clinicians in classifying breast cancer based on ultrasound images. Thus, this system can be developed in mobile, web, or alternative environments and offered as a computer-aided system for the use of radiologists, pathologists or other healthcare professionals in hospitals.

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Transfer Learning for Drug Discovery

Cited by 266 publications

References 84 publications

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes

A Transferable Deep Learning Approach to Fast Screen Potent Antiviral Drugs against SARS-CoV-2

Transfer Learning-Based Classification of Breast Cancer using Ultrasound Images

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