ReviewPharmaceuticals (Basel, Switzerland)2020
Data-Driven Molecular Dynamics: A Multifaceted Challenge.
Review in Pharmaceuticals (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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.
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Who cites it
15 citing papers in PubMed.
- Dissecting the RAD51-BRC4 Interaction Landscape through Integrative Molecular Simulations and Experimental Biophysics.Journal of chemical information and modeling · 2025Article
- A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors.International journal of molecular sciences · 2024Article
- tICA-Metadynamics for Identifying Slow Dynamics in Membrane Permeation.Journal of chemical theory and computation · 2023Article
- Perspectives on Ligand/Protein Binding Kinetics Simulations: Force Fields, Machine Learning, Sampling, and User-Friendliness.Journal of chemical theory and computation · 2023Review
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of theInternational journal of molecular sciences · 2023Article
- SINAPs: A Software Tool for Analysis and Visualization of Interaction Networks of Molecular Dynamics Simulations.Journal of chemical information and modeling · 2022Article
- Challenges and frontiers of computational modelling of biomolecular recognition.QRB discovery · 2022Article
- Computational drug discovery under RNA times.QRB discovery · 2022Review
- AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics.The international journal of high performance computing applications · 2021Article
- Machine Learning and Enhanced Sampling Simulations for Computing the Potential of Mean Force and Standard Binding Free Energy.Journal of chemical theory and computation · 2021Article
- AI in drug development: a multidisciplinary perspective.Molecular diversity · 2021Article
- In Silico Approaches: A Way to Unveil Novel Therapeutic Drugs for Cervical Cancer Management.Pharmaceuticals (Basel, Switzerland) · 2021Review
- How to Determine Accurate Conformational Ensembles by Metadynamics Metainference: A Chignolin Study Case.Frontiers in molecular biosciences · 2021Article
- AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics.bioRxiv : the preprint server for biology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.
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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.