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A hitchhiker's guide to deep chemical language processing for bioactivity prediction.
Digit Discov
December 2024
Eindhoven University of Technology, Institute for Complex Molecular Systems, Eindhoven AI Systems Institute, Dept. Biomedical Engineering Eindhoven Netherlands
Deep learning has significantly accelerated drug discovery, with 'chemical language' processing (CLP) emerging as a prominent approach. CLP approaches learn from molecular string representations (, Simplified Molecular Input Line Entry Systems [SMILES] and Self-Referencing Embedded Strings [SELFIES]) with methods akin to natural language processing. Despite their growing importance, training predictive CLP models is far from trivial, as it involves many 'bells and whistles'.
View Article and Find Full Text PDFA comprehensive dataset on cytotoxicity of ionic liquids.
Sci Data
December 2024
Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow, 119991, Russia.
Ionic liquids (ILs) are structurally tunable salts with applications ranging from chemical synthesis to batteries, novel materials and medicine. Despite their potential, the toxicity of ILs poses significant environmental and biological challenges. This study introduces a comprehensive dataset of cytotoxicity of 1227 ILs, compiled from 151 research papers and encompassing 3837 data entries.
View Article and Find Full Text PDFTransformer-based models for chemical SMILES representation: A comprehensive literature review.
Heliyon
October 2024
Chungbuk National University, Department of Computer Engineering, Cheongju, 28644, South Korea.
Pre-trained chemical language models (CLMs) have attracted increasing attention within the domains of cheminformatics and bioinformatics, inspired by their remarkable success in the natural language processing (NLP) domain such as speech recognition, text analysis, translation, and other objectives associated with language. Furthermore, the vast amount of unlabeled data associated with chemical compounds or molecules has emerged as a crucial research focus, prompting the need for CLMs with reasoning capabilities over such data. Molecular graphs and molecular descriptors are the predominant approaches to representing molecules for property prediction in machine learning (ML).
View Article and Find Full Text PDFPerspectives on Codebook: sequence specificity of uncharacterized human transcription factors.
bioRxiv
November 2024
Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
We describe an effort ("Codebook") to determine the sequence specificity of 332 putative and largely uncharacterized human transcription factors (TFs), as well as 61 control TFs. Nearly 5,000 independent experiments across multiple and assays produced motifs for just over half of the putative TFs analyzed (177, or 53%), of which most are unique to a single TF. The data highlight the extensive contribution of transposable elements to TF evolution, both in and , and identify tens of thousands of conserved, base-level binding sites in the human genome.
View Article and Find Full Text PDFA happy face advantage for pareidolic faces in children and adults.
J Exp Child Psychol
March 2025
Department of Psychology, University of Miami, Coral Gables, FL 33124, USA.
Pareidolic faces-illusory faces in objects-offer a unique context for studying biases in the development of facial processing because they are visually diverse (e.g., color, shape) while lacking key elements of real faces (e.
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