1.361 hasil
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Journal of Cheminformatics
Scopus
ISSN 1758-2946 · BMC · 1.361 artikel di indeks
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Artikel Jurnal
Analysis of cyclohexane, cyclopentane, and benzene conformations in ligands for PDB X-ray structures using the Hill-Reilly approach
Abstract Protein structural data are highly valuable for research, and many significant results have been published on their basis. An important part of protein structures is their ligands. The conformation of rings in ligands is crucial for the ligands’ scaffold and shape and, therefore, for…
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Artikel Jurnal
PepGraphormer: an ESM-GAT hybrid deep learning framework for antimicrobial peptide prediction
Abstract The prediction of Antimicrobial Peptides (AMPs) is a critical research area in drug discovery. Traditional methods, which rely on sequence alignment or handcrafted features, often fail to capture complex sequence-function relationships. Recently, Large Language Models (LLMs) like ESM2 have…
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Artikel Jurnal
Empowering federated learning for robust compound-protein interaction prediction across heterogeneous cross-pharma domains
Abstract Accurately predicting novel compound-protein interactions (CPIs) is essential for accelerating drug discovery. The generalizability of machine learning-based CPI prediction models relies significantly on the availability and diversity of CPI datasets. To maximize data utility, particularly…
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Artikel Jurnal
Optimizing SMILES token sequences via trie-based refinement and transition graph filtering
Abstract Tokenization plays a critical role in preparing SMILES strings for molecular foundation models. Poor token units can fragment chemically meaningful substructures, inflate sequence length, and hinder model learning and interpretability. Existing approaches such as SMILES Pair Encoding (SPE)…
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Artikel Jurnal
Molecular graph-based invariant representation learning with environmental inference and subgraph generation for out-of-distribution generalization
Abstract Molecular representation learning (MRL) is a crucial link between machine learning and chemistry. It plays a vital role in predicting molecular properties and complex tasks such as drug discovery by encoding molecules as numerical vectors. While existing methods perform excellently when…
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Artikel Jurnal
Multimodal graph fusion with statistically guided parsimonious descriptor selection for molecular property prediction
Abstract Graph convolutional networks (GCN) are effective for learning molecular representations, but their reliance on local message passing and simple feature concatenation limits their ability to capture global physicochemical properties. We present KROnecker-product based multimodal fusion with…
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Artikel Jurnal
Proteolysis-targeting Chimera efficacy prediction using a deep-learning–QSP model
Abstract This study presents an integrated computational modeling framework combining deep learning and Quantitative Systems Pharmacology (QSP) to predict the efficacy of PROTAC (PROteolysis Targeting Chimera) molecules. PROTACs have emerged as promising therapeutics for targeted protein…
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Artikel Jurnal
Word embeddings as autonomous predictors in materials design—the effect of inherent variability on information transfer
Abstract We propose that word embeddings of atoms derived from scientific literature are revisited as autonomous machine learning predictors in materials design. If static word embeddings encode comprehensive physicochemical information, joined embeddings of chemical elements constituting a…
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Artikel Jurnal
Ai derivation and exploration of antibiotic class spaces
Abstract Purpose The rapid evolution of antibiotic-resistant bacteria poses an urgent global health crisis. A key gap in current antibiotic discovery approaches is the absence of automated chemical synthesis methods designed to systematically generate and evaluate compounds within specific…
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Artikel Jurnal
Structure-free drug–target affinity prediction using protein and molecule language models
Abstract Accurate prediction of drug-target affinity (DTA) is crucial for advancing drug discovery and optimizing experimental processes. Traditional DTA models often rely on handcrafted features or structural data, which can limit their generalizability and scalability. In this study, we propose a…
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Artikel Jurnal
Universal feature selection for simultaneous interpretability of multitask datasets
Abstract Extracting meaningful features from complex, high-dimensional datasets across scientific domains remains challenging. Current methods often struggle with scalability, limiting their applicability to large datasets, or make restrictive assumptions about feature-property relationships…
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Artikel Jurnal
Chemical genomics language model toward reliable and explainable compound-protein interaction exploration
Abstract Accurate prediction of compound-protein interactions (CPIs) is crucial for chemical biology and drug discovery. Despite recent advancements, existing deep learning (DL)-based CPI models often struggle to simultaneously achieve high generalization performance, quantify prediction…
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Artikel Jurnal
Advancing chemical safety prediction: an integrated GNN framework with DFT-augmented cyclic compound solution
Abstract The rapid proliferation of chemical substances presents significant challenges in assessing their safety–critical physicochemical properties. This study presents an integrated approach using Graph Neural Networks (GNNs) to predict three crucial properties for chemical safety assessment…
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Artikel Jurnal
Learnable protein representations in computational biology for predicting drug-target affinity
Abstract In this review, we discuss the various different types of learnable protein representations that have been used in computational biology, with a particular focus on representations that have been used in the paradigm of predicting drug-target affinity. We explore this from multiple…
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Artikel Jurnal
STRIKER: a spectral metadata repairing tool for expanding the comprehensiveness of spectral libraries
Abstract The expansion of untargeted metabolomics has made publicly accessible spectral libraries indispensable for metabolite annotation and machine learning applications. Enhancing the quality and consistency of these libraries is crucial for improving the accuracy of metabolite identification…
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Artikel Jurnal
A comprehensive evaluation of advanced methods for identifying structural alerts using extensive toxicity data
Abstract With the disclosure of the important role of substructural alerts (SA) in drug development and toxicity evaluation, many automatic substructure extraction tools based on different theoretical knowledge have been reported in recent years. To compare the emphasis of various substructure…
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Artikel Jurnal
DockM8: an all-in-one open-source platform for consensus virtual screening in drug design
Abstract Virtual screening remains a critical step in structure-based drug design, yet variability in docking algorithms and scoring functions often limits its reliability. To address this challenge, we introduce DockM8, an open-source platform for consensus virtual screening that integrates pocket…
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Artikel Jurnal
Multimodal feature fusion for molecular property classification
Abstract Accurate molecular property prediction is a cornerstone of modern chemical science, driving progress in drug discovery, materials design, and environmental research. Yet, most existing models remain unimodal, while multimodal approaches often rely on simple aggregation, leaving much of the…
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