ReviewCommunications chemistry2024
Molecular fragmentation as a crucial step in the AI-based drug development pathway.
Review in Communications chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Deep learning-driven discovery of anti-metastatic phytochemicals targeting MMP-1 in breast cancer via advanced contrastive learning and structural attention.Molecular diversity · 2026Article
- CRUSH-Cleavage Rules Using SMIRKS Heuristics: an enhanced molecular fragmentation algorithm.Journal of cheminformatics · 2026Article
- MolMod: a molecular modification platform for molecular property optimization via fragment-based generation.Molecular diversity · 2026Article
- AI-driven generative framework integrating ML-QSAR and fragment learning for isoform-selective PI3K inhibitor design.Molecular diversity · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids.Briefings in bioinformatics · 2025Article
- Enzyme inhibitory activity of marine alkaloid aaptamines for neurodegenerative diseases: anRoyal Society open science · 2025Article
- Recent advances in molecular representation methods and their applications in scaffold hopping.npj drug discovery · 2025Review
- Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction.Journal of cheminformatics · 2025Article
- Knowledge-aware contrastive heterogeneous molecular graph learning.PLoS computational biology · 2025Article
- Data-driven parametrization of molecular mechanics force fields for expansive chemical space coverage.Chemical science · 2025Article
- fragSMILES as a chemical string notation for advanced fragment and chirality representation.Communications chemistry · 2025Article
- Unveiling the molecular mechanisms of stigmasterol on diabetic retinopathy: BNM framework construction and experimental validation.Frontiers in medicine · 2025Article
- Fragment-based drug discovery: A graphical review.Current research in pharmacology and drug discovery · 2025Review
- Exploring Extended Warheads toward Developing Cysteine-Targeted Covalent Kinase Inhibitors.Journal of chemical information and modeling · 2024Article
- Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability.Journal of cheminformatics · 2024Article
- NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer.Molecules (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.