Virtual molecular catalogs have limited utility if member compounds are (i) difficult to synthesize or (ii) unlikely to have biological activity. The Distributed Drug Discovery (D3) program addresses the synthesis challenge by providing scientists with a free virtual D3 catalog of 73,024 easy-to-synthesize N-acyl unnatural α-amino acids, their methyl esters, and primary amides. The remaining challenge is to document and exploit the bioactivity potential of these compounds. In the current work, a search process is described that retrospectively identifies all virtual D3 compounds classified as bioactive hits in PubChem-cataloged experimental assays. The results provide insight into the broad range of drug-target classes amenable to inhibition and/or agonism by D3-accessible molecules. To encourage computer-aided drug discovery centered on these compounds, a publicly available virtual database of D3 molecules prepared for use with popular computer docking programs is also presented.
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http://dx.doi.org/10.1111/cbdd.13012 | DOI Listing |
Brief Bioinform
November 2024
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.
View Article and Find Full Text PDFNPJ Biofilms Microbiomes
January 2025
A*STAR Skin Research Labs (A*SRL), Agency for Science, Technology, and Research (A*STAR) & Skin Research Institute of Singapore (SRIS), Singapore, Republic of Singapore.
Sebaceous free fatty acids are metabolized by multiple skin microbes into bioactive lipid mediators termed oxylipins. This study investigated correlations between skin oxylipins and microbes on the superficial skin of pre-pubescent children (N = 36) and adults (N = 100), including pre- (N = 25) and post-menopausal females (N = 25). Lipidomics and metagenomics revealed that Malassezia restricta positively correlated with the oxylipin 9,10-DiHOME on adult skin and negatively correlated with its precursor, 9,10-EpOME, on pre-pubescent skin.
View Article and Find Full Text PDFCell Death Discov
January 2025
The Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada.
Lin28 is a key regulator of cancer stem cell gene network that promotes therapy-resistant tumor progression in various tumors. However, no Lin28 inhibitor has been approved to treat cancer patients, urging exploration of novel compounds as candidates to be tested for clinical trials. In this contribution, we applied computer-aided drug design (CADD) in combination with quantitative biochemical and biological assays.
View Article and Find Full Text PDFJ Biomed Sci
January 2025
Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, State Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, 510006, China.
Background: Recent studies indicate that N6-methyladenosine (mA) RNA modification may regulate ferroptosis in cancer cells, while its molecular mechanisms require further investigation.
Methods: Liquid Chromatography-Tandem Mass Spectrometry (HPLC/MS/MS) was used to detect changes in mA levels in cells. Transmission electron microscopy and flow cytometry were used to detect mitochondrial reactive oxygen species (ROS).
BMC Bioinformatics
January 2025
School of Information and Artificial Intelligence, Anhui Agricultural University, Changjiang West Road, Hefei, 230036, Anhui, China.
Drug-target interactions (DTIs) are pivotal in drug discovery and development, and their accurate identification can significantly expedite the process. Numerous DTI prediction methods have emerged, yet many fail to fully harness the feature information of drugs and targets or address the issue of feature redundancy. We aim to refine DTI prediction accuracy by eliminating redundant features and capitalizing on the node topological structure to enhance feature extraction.
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