ArticleChemMedChem2018
IChem: A Versatile Toolkit for Detecting, Comparing, and Predicting Protein-Ligand Interactions.
Article in ChemMedChem, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Identification of potential SARS-CoV-2 MJournal of computer-aided molecular design · 2026Article
- Structure-Guided Discovery of CHI3L1 Inhibitors from Ultralarge Chemical Spaces for Glioblastoma Therapy.ACS omega · 2026Article
- LigExtract: Large-scale Automated Identification of Ligands from Protein Structures in the Protein Data Bank.Genomics, proteomics & bioinformatics · 2025Article
- Solvation Free Energies of Drug-like Molecules via Fast Growth in an Explicit Solvent: Assessment of the AM1-BCC, RESP/HF/6-31G*, RESP-QM/MM, and ABCG2 Fixed-Charge Approaches.Journal of chemical theory and computation · 2025Article
- InDeepNet: a web platform for predicting functional binding sites in proteins using InDeep.Nucleic acids research · 2025Article
- Towards a comprehensive view of the pocketome universe-biological implications and algorithmic challenges.PLoS computational biology · 2025Article
- Assessing interaction recovery of predicted protein-ligand poses.Journal of cheminformatics · 2025Article
- GRADE and X-GRADE: Unveiling Novel Protein-Ligand Interaction Fingerprints Based on GRAIL Scores.Journal of chemical information and modeling · 2025Article
- Structural basis of positive allosteric modulation of metabotropic glutamate receptor activation and internalization.Nature communications · 2024Article
- LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification.Journal of cheminformatics · 2024Article
- Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces.ACS central science · 2024Article
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.Biomolecules · 2024Review
- The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.International journal of molecular sciences · 2023Article
- How Ligands Interact with the Kinase Hinge.ACS medicinal chemistry letters · 2023Article
- A practical guide to machine-learning scoring for structure-based virtual screening.Nature protocols · 2023Review
- De Novo Design of κ-Opioid Receptor Antagonists Using a Generative Deep-Learning Framework.Journal of chemical information and modeling · 2023Article
- Mining the Protein Data Bank to inspire fragment library design.Frontiers in chemistry · 2023Article
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence.Journal of cheminformatics · 2022Article
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Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structure-based ligand design requires an exact description of the topology of molecular entities under scrutiny. IChem is a software package that reflects the many contributions of our research group in this area over the last decade. It facilitates and automates many tasks (e.g., ligand/cofactor atom typing, identification of key water molecules) usually left to the modeler's choice. It therefore permits the detection of molecular interactions between two molecules in a very precise and flexible manner. Moreover, IChem enables the conversion of intricate three-dimensional (3D) molecular objects into simple representations (fingerprints, graphs) that facilitate knowledge acquisition at very high throughput. The toolkit is an ideal companion for setting up and performing many structure-based design computations.
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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.