Computational Biology and Chemistry This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

FaissMolLib: An efficient and easy deployable tool for ligand-based virtual screening
Comput Biol Chem. 2024 Apr 1;110:108057. doi: 10.1016/j.compbiolchem.2024.108057. Online ahead of print.ABSTRACTVirtual screening-based molecular similarity and fingerprint are crucial in drug design, target prediction, and ADMET prediction, aiding in identifying potential hits and optimizing lead compounds. However, challenges such as lack of comprehensive open-source molecular fingerprint databases and efficient search methods for virtual screening are prevalent. To address these issues, we introduce FaissMolLib, an open-source virtual screening tool that integrates 2.8 million compounds from ChEMBL and ZINC databases. N...
Source: Computational Biology and Chemistry - April 6, 2024 Category: Bioinformatics Authors: Haihan Liu Peiying Chen Baichun Hu Shizun Wang Hanxun Wang Jiasi Luan Jian Wang Bin Lin Maosheng Cheng Source Type: research

Accuracy of AlphaFold models: Comparison with short N … O contacts in atomic resolution protein crystal structures
Comput Biol Chem. 2024 Apr 4;110:108069. doi: 10.1016/j.compbiolchem.2024.108069. Online ahead of print.ABSTRACTArtificial intelligence (AI) has revolutionized structural biology by predicting protein 3D structures with near-experimental accuracy. Here, short backbone N-O distances in high-resolution crystal structures were compared to those in three-dimensional models based on AI AlphaFold/ColabFold, specifically considering their estimated standard errors. Experimental and computationally modeled distances very often differ significantly, showing that these models' precision is inadequate to reproduce experimental result...
Source: Computational Biology and Chemistry - April 6, 2024 Category: Bioinformatics Authors: Oliviero Carugo Source Type: research

K 1 K 2 NN: A novel multi-label classification approach based on neighbors for predicting COVID-19 drug side effects
Comput Biol Chem. 2024 Apr 2;110:108066. doi: 10.1016/j.compbiolchem.2024.108066. Online ahead of print.ABSTRACTCOVID-19, a novel ailment, has received comparatively fewer drugs for its treatment. Side Effects (SE) of a COVID-19 drug could cause long-term health issues. Hence, SE prediction is essential in COVID-19 drug development. Efficient models are also needed to predict COVID-19 drug SE since most existing research has proposed many classifiers to predict SE for diseases other than COVID-19. This work proposes a novel classifier based on neighbors named K1 K2 Nearest Neighbors (K1K2NN) to predict the SE of the COVID-...
Source: Computational Biology and Chemistry - April 5, 2024 Category: Bioinformatics Authors: Pranab Das Dilwar Hussain Mazumder Source Type: research

Predicting associations between drugs and G protein-coupled receptors using a multi-graph convolutional network
In this study, based on a multi-graph convolutional network, an end-to-end deep model was developed to efficiently and precisely discover latent drug-GPCR relationships by combining data from multi-sources. We demonstrated that our model, based on multi-graph convolutional networks, outperformed rival deep learning techniques as well as non-deep learning models in terms of inferring drug-GPCR relationships. Our results indicated that integrating data from multi-sources can lead to further advancement.PMID:38579550 | DOI:10.1016/j.compbiolchem.2024.108060 (Source: Computational Biology and Chemistry)
Source: Computational Biology and Chemistry - April 5, 2024 Category: Bioinformatics Authors: Yuxun Luo Shasha Li Li Peng Pingjian Ding Wei Liang Source Type: research

Prediction of viral protease inhibitors using proteochemometrics approach
Comput Biol Chem. 2024 Mar 24;110:108061. doi: 10.1016/j.compbiolchem.2024.108061. Online ahead of print.ABSTRACTBeing widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsi...
Source: Computational Biology and Chemistry - April 4, 2024 Category: Bioinformatics Authors: Dmitry A Karasev Boris N Sobolev Dmitry A Filimonov Alexey Lagunin Source Type: research

DeepPLM_mCNN: An approach for enhancing ion channel and ion transporter recognition by multi-window CNN based on features from pre-trained language models
This study illustrates the potential of combining PLMs and deep learning for accurate computational identification of membrane proteins from sequence data alone. Our findings have important implications for membrane protein research and drug development targeting ion channels and transporters. The data and source codes in this study are publicly available at the following link: https://github.com/s1129108/DeepPLM_mCNN.PMID:38555810 | DOI:10.1016/j.compbiolchem.2024.108055 (Source: Computational Biology and Chemistry)
Source: Computational Biology and Chemistry - March 31, 2024 Category: Bioinformatics Authors: Van-The Le Muhammad-Shahid Malik Yi-Hsuan Tseng Yu-Cheng Lee Cheng-I Huang Yu-Yen Ou Source Type: research

A cloud-based precision oncology framework for whole genome sequence analysis
Comput Biol Chem. 2024 Mar 28;110:108062. doi: 10.1016/j.compbiolchem.2024.108062. Online ahead of print.ABSTRACTCancer is one of the wide-ranging diseases which have a high mortality rate impacting globally. This scenario can be switched by early detection and correct precision treatment, a major concern for cancer patients. Clinicians can figure out the best-suited treatments for cancer patients by analyzing the patient's genome, which will treat the patient well and minimize the chances of side effects as well. Therefore, we have developed a fast, robust, and efficient solution as our precision oncology framework based ...
Source: Computational Biology and Chemistry - March 30, 2024 Category: Bioinformatics Authors: Saloni Tandon Medha Sharma Pratik Kasar Anirudh Kala Source Type: research

SNSynergy: Similarity network-based machine learning framework for synergy prediction towards new cell lines and new anticancer drug combinations
Comput Biol Chem. 2024 Mar 19;110:108054. doi: 10.1016/j.compbiolchem.2024.108054. Online ahead of print.ABSTRACTThe computational method has been proven to be a promising means for pre-screening large-scale anticancer drug combinations to support precision oncology applications. Pioneering efforts have been made to develop machine learning technology for predicting drug synergy, but high computational cost for training models as well as great diversity and limited size in screening data escalate the difficulty of prediction. To address this challenge, we propose a simple machine learning framework, namely Similarity Netwo...
Source: Computational Biology and Chemistry - March 24, 2024 Category: Bioinformatics Authors: Xiaosheng Huangfu Chengwei Zhang Hualong Li Sile Li Yushuang Li Source Type: research