ArticleScientific reports2017
Adaptive simulations, towards interactive protein-ligand modeling.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed, 79 citations in OpenAlex.
- Identification of new dasatinib analogues targeting mutated BCR-ABL1: virtual screening, molecular docking, and dynamics simulations studies.Molecular diversity · 2026Article
- Structural insights into a conserved mechanism of choline translocation through CHT.Science advances · 2026Article
- A la carte bioprospecting of substrate-selective laccases via high-throughput computational enzyme-substrate interaction profiling.Protein science : a publication of the Protein Society · 2026Article
- EngineeringiScience · 2026Article
- An image-based protein-ligand binding representation learning framework via multi-level flexible dynamics trajectory pre-training.Bioinformatics (Oxford, England) · 2025Article
- Optimizing drug design by merging generative AI with a physics-based active learning framework.Communications chemistry · 2025Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Article
- Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites.Journal of chemical theory and computation · 2024Article
- Structure and mechanisms of transport of human Asc1/CD98hc amino acid transporter.Nature communications · 2024Article
- Molecular docking, molecular dynamics simulations and binding free energy studies of interactions between Mycobacterium tuberculosis Pks13, PknG and bioactive constituents of extremophilic bacteria.Scientific reports · 2024Article
- Profile of a Multivariate Observation under Destructive Sampling-A Monte Carlo Approach to a Case of Spina Bifida.Bioengineering (Basel, Switzerland) · 2024Article
- Enhancing the Hydrolytic Activity of a Lipase towards Larger Triglycerides through Lid Domain Engineering.International journal of molecular sciences · 2023Article
- Selective inhibitors of the PSEN1-gamma-secretase complex.The Journal of biological chemistry · 2023Article
- AsiteDesign: a Semirational Algorithm for an Automated Enzyme Design.The journal of physical chemistry. B · 2023Article
- Recent PELE Developments and Applications in Drug Discovery Campaigns.International journal of molecular sciences · 2022Review
- Design of Artificial Enzymes Bearing Several Active Centers: New Trends, Opportunities and Problems.International journal of molecular sciences · 2022Review
- Small molecule interactions with the SARS-CoV-2 main protease: In silico all-atom microsecond MD simulations, PELE Monte Carlo simulations, and determination of in vitro activity inhibition.Journal of molecular graphics & modelling · 2022Article
- Metabolization and sequestration of plant specialized metabolites in insect herbivores: Current and emerging approaches.Frontiers in physiology · 2022Review
- Structural basis for substrate specificity of heteromeric transporters of neutral amino acids.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.
Indexed as
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.