Suchergebnisse - Improving Reproducibility and Usability in the Journal of Cheminformatics*

Computational Landscape in Drug Discovery: From AI/ML Models to Translational Application.

Autoren: Sharma, Deepak Anabala, Madhu Jain, V. Vanitha et al.

Quelle: Scientifica; 11/24/2025, Vol. 2025, p1-21, 21p

Schlagwörter: ARTIFICIAL intelligence, DRUG discovery, PHARMACEUTICAL chemistry, STATISTICAL decision making, REINFORCEMENT learning, TRANSLATIONAL research, MATHEMATICAL optimization

Open-Source Software Development in Cheminformatics: A Qualitative Analysis of Rationales.

Autoren: Pernaa, Johannes Takala, Aleksi Ciftci, Veysel et al.

Quelle: Applied Sciences (2076-3417); Sep2023, Vol. 13 Issue 17, p9516, 21p

Schlagwörter: COMPUTER software development, CHEMINFORMATICS, SOFTWARE engineering, OPEN scholarship, EDUCATIONAL objectives, INTERDISCIPLINARY research

TICTAC: target illumination clinical trial analytics with cheminformatics.

Autoren: Abok, Jeremiah I. Edwards, Jeremy S. Yang, Jeremy J.

Quelle: Frontiers in Bioinformatics; 2025, p1-11, 11p

Quantum computing revolution in healthcare: a systematic review of applications, issues and future directions.

Autoren: Bukkarayasamudram, Vamshi Krishna Reddy, Pundru Chandra Shaker Arun Kumar, K et al.

Quelle: Artificial Intelligence Review; Dec2025, Vol. 58 Issue 12, p1-35, 35p

Advancing human health risk assessment: the role of new approach methodologies.

Autoren: Deepika, Deepika Bharti, Kanchan Sharma, Shubh et al.

Quelle: Frontiers in Toxicology; 2025, p1-18, 18p

Schlagwörter: RISK assessment, TOXICITY testing, REGULATORY compliance, MATHEMATICAL models, CELL culture, BIOCHEMICAL mechanism of action, PUBLIC health

Exploring AI-enhanced NMR dereplication analysis for complex mixtures and its potential use in adulterant detection.

Autoren: Du, Xiao-Nan Chen, You-Wen Wang, Qing et al.

Quelle: Phytochemistry Reviews; Aug2025, Vol. 24 Issue 4, p2833-2868, 36p

Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions.

Autoren: Abdelwahab, Asmaa A. Elattar, Mustafa A. Fawzi, Sahar Ali

Quelle: BioMedical Engineering OnLine; 7/23/2025, Vol. 24 Issue 1, p1-34, 34p

Schlagwörter: CYTOCHROME P-450 CYP3A, DRUG discovery, GRAPH theory, MACHINE learning, METABOLISM, COMPUTATIONAL intelligence, GRAPH neural networks, PROTEIN isoforms

AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management.

Autoren: Xian, Yuehui Li, Cheng Xu, Yangyang et al.

Quelle: SusMat; Oct2025, Vol. 5 Issue 5, p1-37, 37p

Schlagwörter: ARTIFICIAL intelligence, ENERGY conversion, BIODEGRADABLE materials, ENERGY dissipation, CONDITION-based maintenance, ELECTRODES, CLEAN energy, PRODUCT life cycle

Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.

Autoren: Moreira-Filho, José T. Ranganath, Dhruv Conway, Mike et al.

Quelle: Journal of Cheminformatics; 8/16/2024, Vol. 16 Issue 1, p1-27, 27p

Schlagwörter: MACHINE learning, FEATURE selection, DATA analytics, ARTIFICIAL intelligence, DATA mining

GraphDrug: A Multimodal Graph Learning for Predicting Bioactivity and Pharmacokinetics of Drug Candidates.

Autoren: Das, Madhu Sudan Gogoi, Usha Rani Anu Samanta

Quelle: International Journal of Environmental Sciences (2229-7359); 2025 Special Issue, Vol. 11, p1134-1145, 12p

Schlagwörter: GRAPH neural networks, PHARMACOKINETICS, DRUG discovery, MULTIVALENT molecules, DRUG efficacy

Deep learning reveals endogenous sterols as allosteric modulators of the GPCR–Gα interface.

Autoren: Mohanty, Sanjay Kumar Mittal, Aayushi Farooqi, Namra et al.

Quelle: eLife; 12/8/2025, Vol. 14, p1-38, 38p

A systematic review of multi-modal large language models on domain-specific applications.

Autoren: Li, Sirui Wong, Kok Wai Wang, Guanjin et al.

Quelle: Artificial Intelligence Review; Dec2025, Vol. 58 Issue 12, p1-47, 47p

A Functionally-Grounded Benchmark Framework for XAI Methods: Insights and Foundations from a Systematic Literature Review.

Autoren: Canha, Dulce Kubler, Sylvain Främling, Kary et al.

Quelle: ACM Computing Surveys; Dec2025, Vol. 57 Issue 12, p1-40, 40p

An Analysis Of The Concept Of Law And Its Importance In Society And Education Of Jean-Jacques Rousseau.

Autoren: P. P., Yathukrishna Neena, T. S.

Quelle: Journal of Advanced Zoology. 2024, Vol. 45 Issue 2, p42-44. 3p.

Drug design and discovery with bioinformatics tools.

Alternate Title: Diseño y descubrimiento de fármacos con herramientas bioinformáticas. (Spanish)
Projeto e descoberta de fármacos com ferramentas de bioinformática. (Portuguese)

Autoren: Oraibi, Amjad I. Zheoat, Ahmed Mohammed Sameer, Hayder Naji et al.

Quelle: Revista Colombiana de Ciencias Químico-Farmacéuticas; 2025, Vol. 54 Issue 3, p621-642, 22p

Schlagwörter: DRUG design, DRUG discovery, BIOINFORMATICS, PATIENT safety, MOLECULAR docking, DRUG target, PHARMACOLOGY, MATHEMATICAL optimization

Current practice, challenges and future opportunities in the safety assessment of newly expressed proteins in genetically modified plants.

Autoren: Casacuberta, Josep Barro, Francisco Braeuning, Albert et al.

Quelle: EFSA Journal; Aug2025, Vol. 23 Issue 8, p1-40, 40p

Schlagwörter: SAFETY, RISK assessment, TRANSGENIC organisms, PROTEIN expression, FOOD safety, TRANSGENIC plants, FOOD safety laws, SYSTEMS theory

Digital Alchemy: The Rise of Machine and Deep Learning in Small-Molecule Drug Discovery.

Autoren: Manan, Abdul Baek, Eunhye Ilyas, Sidra et al.

Quelle: International Journal of Molecular Sciences; Jul2025, Vol. 26 Issue 14, p6807, 42p

Schlagwörter: ARTIFICIAL intelligence, DRUG discovery, PHARMACEUTICAL technology, DRUG design, VIRTUAL high-throughput screening (Drug development), MACHINE learning, DEEP learning

Recent Applications of Explainable AI (XAI): A Systematic Literature Review.

Autoren: Saarela, Mirka Podgorelec, Vili

Quelle: Applied Sciences (2076-3417); Oct2024, Vol. 14 Issue 19, p8884, 111p

Schlagwörter: CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, MACHINE learning, DEEP learning, WEB databases

BioBricks.ai: a versioned data registry for life sciences data assets.

Autoren: Gao, Yifan Mughal, Zakariyya Jaramillo-Villegas, Jose A. et al.

Quelle: Frontiers in Artificial Intelligence; 2025, p1-10, 10p

The Artificial Intelligence-Driven Intelligent Laboratory for Organic Chemistry Synthesis.

Autoren: Li, Tan Song, Weining Chen, Nanjiang et al.

Quelle: Applied Sciences (2076-3417); Jul2025, Vol. 15 Issue 13, p7387, 24p

Schlagwörter: ARTIFICIAL intelligence, ORGANIC chemistry, SCIENTIFIC apparatus & instruments, AUTOMATION, CLOUD computing, CHEMICAL synthesis, MACHINE learning, LABORATORIES