Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity according to the scale of datasets, especially for small scale datasets, and is robust with few hyper-parameters and without over-fitting. We found that the proposed model is superior to other CPI prediction models and performs well on the constructed challenging dataset. We finally predicted 2 new inhibitors for clusters of differentiation 47(CD47) which has few known inhibitors. The ICs of enzyme activities of these 2 new small molecular inhibitors targeting CD47-SIRPα interaction are 3.57 and 4.79 μM respectively. These results fully demonstrate the competence of this concise but efficient tool for CPI prediction.
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http://dx.doi.org/10.3389/fchem.2023.1292869 | DOI Listing |
Comput Biol Chem
December 2024
School of Software, Henan Polytechnic University, Jiaozuo 454003, China. Electronic address:
Background: Compound-protein interaction (CPI) is essential to drug discovery and design, where traditional methods are often costly and have low success rates. Recently, the integration of machine learning and deep learning in CPI research has shown potential to reduce costs and enhance discovery efficiency by improving protein target identification accuracy. Additionally, with an urgent need for novel therapies against complex diseases, CPI investigation could lead to the identification of effective new drugs.
View Article and Find Full Text PDFCureus
October 2024
Department of Clinical Analysis, College of Pharmacy, Hawler Medical University, Erbil, IRQ.
Bioinformatics
November 2024
Department of Big Data and Biomedical AI, College of Future Technology, Peking University, Beijing 100871, China.
Motivation: Accurate prediction of drug-target interactions (DTIs), especially for novel targets or drugs, is crucial for accelerating drug discovery. Recent advances in pretrained language models (PLMs) and multi-modal learning present new opportunities to enhance DTI prediction by leveraging vast unlabeled molecular data and integrating complementary information from multiple modalities.
Results: We introduce DrugLAMP (PLM-assisted multi-modal prediction), a PLM-based multi-modal framework for accurate and transferable DTI prediction.
Discov Oncol
November 2024
Department of Pediatric Surgery, The Affiliated Taian City Central Hospital of Qingdao University, No.29 Longtan Road, Taishan District, Taian, 271000, Shandong Province, People's Republic of China.
Background: Neuroblastoma (NB), the most common extracranial solid tumor in children, is featured by high malignancy and poor prognosis. Flavokawain A (FKA), a novel chalcone isolated from the roots of the kava plant, has been identified to exert the tumor-inhibiting properties in various cancers. The present study was formulated to tell about the anticarcinogenic effects of FKA against NB and to thoroughly elucidate the intrinsic molecular mechanisms.
View Article and Find Full Text PDFAquac Nutr
December 2023
Poyang Lake Fisheries Research Centre of Jiangxi Province, Jiangxi Fisheries Research Institute, Nanchang 330039, China.
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