scholarly journals Data-Driven Molecular Dynamics: A Multifaceted Challenge

2020 ◽  
Vol 13 (9) ◽  
pp. 253
Author(s):  
Mattia Bernetti ◽  
Martina Bertazzo ◽  
Matteo Masetti
Keyword(s):  
Molecular Dynamics ◽  
Big Data ◽  
Drug Discovery ◽  
Drug Design ◽  
Md Simulations ◽  
Biomolecular Systems ◽  
Biomolecular Simulations ◽  
The Impact ◽  
Opening Up ◽  
Computational Drug Discovery

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.

Extracting atomic contributions to binding free energy from molecular dynamics simulations with mixed solvents (MDmix)

2021 ◽  
Vol 19 ◽  
Author(s):  
Daniel Alvarez- Garcia ◽  
Peter Schmidtke ◽  
Elena Cubero ◽  
Xavier Barril
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Drug Discovery ◽  
Drug Design ◽  
Binding Free Energy ◽  
Mixed Solvents ◽  
Md Simulations ◽  
Molecular Binding ◽  
Dynamics Simulations ◽  
Energy Maps

Background: Mixed solvents MD simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery Method: Mixed solvents MD simulations have proved to be a useful and increasingly accepted technique with several applications in structure-based drug discovery Result: As such, they are hardly transferable to different molecules. Conclusion: To achieve transferable energies, we present here a method for decomposing the molecular binding free energy into accurate atomic contributions and we demonstrate with two qualitative visual examples how the corrected energy maps better match known binding hotspots and how they can reveal hidden hotspots with actual drug design potential.

scholarly journals Big Data Approach to Large Scale Molecular Dynamics Simulations: Necessity and Inevitability for Drug Design

2021 ◽  
Author(s):  
Dheeraj Chitara ◽  
Sanjeev B. S.
Keyword(s):  
Molecular Dynamics ◽  
Big Data ◽  
Drug Design ◽  
Large Scale ◽  
Md Simulations ◽  
Complete Analysis ◽  
Cloud Infrastructure ◽  
Drug Receptor ◽  
Dynamics Simulations ◽  
Available Resources

Molecular Dynamics (MD) simulations model motion of molecules in atomistic detail and aid in drug design. While simulations on large systems may require several days to complete, analysis of terabytes of data generated in the process could also be time consuming. Recent studies captured exciting and dramatic drug-receptor interactions under cell-like complex conditions. Such advances make simulations of biomolecular interactions more realistic, insightful, and informative and have potential to make drug design more realistic. However, currently available resources and techniques do not provide, in reasonable time, a comprehensive understanding of events seen in simulations. We demonstrate that big data approach results in significant speedups, and provides rapid insights into simulations performed. Advancing this improvement, we propose a scalable, self-tuning, and responsive framework based on Cloud-infrastructure to accomplish the best possible MD studies with given priorities and within available resources.

scholarly journals Structural Properties of G,T-Parallel Duplexes

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Anna Aviñó ◽  
Elena Cubero ◽  
Raimundo Gargallo ◽  
Carlos González ◽  
Modesto Orozco ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Structural Properties ◽  
Theoretical Calculations ◽  
Md Simulations ◽  
Theoretical Studies ◽  
Duplex Structure ◽  
Strong Stabilization ◽  
The Stability ◽  
The Impact

The structure of G,T-parallel-stranded duplexes of DNA carrying similar amounts of adenine and guanine residues is studied by means of molecular dynamics (MD) simulations and UV- and CD spectroscopies. In addition the impact of the substitution of adenine by 8-aminoadenine and guanine by 8-aminoguanine is analyzed. The presence of 8-aminoadenine and 8-aminoguanine stabilizes the parallel duplex structure. Binding of these oligonucleotides to their target polypyrimidine sequences to form the corresponding G,T-parallel triplex was not observed. Instead, when unmodified parallel-stranded duplexes were mixed with their polypyrimidine target, an interstrand Watson-Crick duplex was formed. As predicted by theoretical calculations parallel-stranded duplexes carrying 8-aminopurines did not bind to their target. The preference for the parallel-duplex over the Watson-Crick antiparallel duplex is attributed to the strong stabilization of the parallel duplex produced by the 8-aminopurines. Theoretical studies show that the isomorphism of the triads is crucial for the stability of the parallel triplex.

scholarly journals Re-Designing Hazardous Chemicals with Target-Specific Toxicities by Learning from Structure-Based Drug Design

2018 ◽  
Author(s):  
Traci Clymer ◽  
Vanessa Vargas ◽  
Eric Corcoran ◽  
Robin Kleinberg ◽  
Jakub Kostal
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Adverse Effects ◽  
Environmental Health ◽  
In Silico ◽  
Adverse Outcomes ◽  
Design Stage ◽  
Hazardous Chemicals ◽  
Structure Based Drug Design ◽  
Computational Drug Discovery

Chemicals are the basis of our society and economy, yet many existing chemicals are known to have unintended adverse effects on human and environmental health. Testing all existing and new chemicals on animals is both economically and ethically unfeasible. In this paper, a new in silico framework is presented that affords redesign of existing hazardous chemicals in commerce based on specific molecular initiating events in their adverse outcomes pathways. Our approach is based on a successful methodology implemented in computational drug discovery, and promises to dramatically lower costs associated with new chemical development by synergistically addressing chemical function and safety at the design stage. <br>

scholarly journals Understanding the Pyrimethamine Drug Resistance Mechanism via Combined Molecular Dynamics and Dynamic Residue Network Analysis

Molecules ◽  
2020 ◽  
Vol 25 (4) ◽  
pp. 904 ◽  
Author(s):  
Arnold Amusengeri ◽  
Rolland Bantar Tata ◽  
Özlem Tastan Bishop
Keyword(s):  
Molecular Dynamics ◽  
Drug Resistance ◽  
Network Analysis ◽  
Binding Free Energy ◽  
Resistance Mechanism ◽  
Point Mutations ◽  
Md Simulations ◽  
Conformational Plasticity ◽  
New Perspective ◽  
The Impact

In this era of precision medicine, insights into the resistance mechanism of drugs are integral for the development of potent therapeutics. Here, we sought to understand the contribution of four point mutations (N51I, C59R, S108N, and I164L) within the active site of the malaria parasite enzyme dihydrofolate reductase (DHFR) towards the resistance of the antimalarial drug pyrimethamine. Homology modeling was used to obtain full-length models of wild type (WT) and mutant DHFR. Molecular docking was employed to dock pyrimethamine onto the generated structures. Subsequent all-atom molecular dynamics (MD) simulations and binding free-energy computations highlighted that pyrimethamine’s stability and affinity inversely relates to the number of mutations within its binding site and, hence, resistance severity. Generally, mutations led to reduced binding affinity to pyrimethamine and increased conformational plasticity of DHFR. Next, dynamic residue network analysis (DRN) was applied to determine the impact of mutations and pyrimethamine binding on communication dispositions of DHFR residues. DRN revealed residues with distinctive communication profiles, distinguishing WT from drug-resistant mutants as well as pyrimethamine-bound from pyrimethamine-free models. Our results provide a new perspective on the understanding of mutation-induced drug resistance.

Molecular Dynamics Study Of Surface Morphological Evolution By Cluster Impacts

2003 ◽  
Vol 792 ◽  
Author(s):  
Takaaki Aoki ◽  
Jiro Matsuo ◽  
Isao Yamada
Keyword(s):  
Molecular Dynamics ◽  
Ion Irradiation ◽  
Morphological Evolution ◽  
Md Simulations ◽  
Impact Area ◽  
Cluster Ion ◽  
Dynamics Simulations ◽  
Large Clusters ◽  
The Hill ◽  
The Impact

ABSTRACTIn order to understand the characteristics of surface modification process with cluster ion irradiation, molecular dynamics simulations of Ar cluster impacting on Si surface with various surface structures were carried out. It was found that the surface morphology is dynamically deformed with only one cluster impact and the impact process of cluster is different depending on the local surface structure. For example, when an Ar2000 cluster accelerated with 20keV impacted on the convex point of the surface, the hill was compressed and the impact area was smoothed. At the impact on a concave point, a deeper crater was formed compared with the impact on a flat surface. On the other hand, the MD simulations of sequential impacts of large clusters were performed. It was found that the small tip structures on the surface could be removed easily with cluster irradiation. It was shown that surface roughness converges to 15∼20Å and this value agrees with the result obtained by single impact of cluster.

scholarly journals Correction to: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

2018 ◽  
Vol 20 (4) ◽  
Author(s):  
Yankang Jing ◽  
Yuemin Bian ◽  
Ziheng Hu ◽  
Lirong Wang ◽  
Xiang-Qun Xie
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design

scholarly journals The Effects of Combinatorial Chemistry and Technologies on Drug Discovery and Biotechnology – a Mini Review

2014 ◽  
Vol 13 (2) ◽  
pp. 87-108 ◽  
Author(s):  
Pierfausto Seneci ◽  
Giorgio Fassina ◽  
Vladimir Frecer ◽  
Stanislav Miertus
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Expert Opinion ◽  
High Throughput ◽  
Combinatorial Chemistry ◽  
Rational Design ◽  
Combinatorial Libraries ◽  
Computer Assisted ◽  
Future Trends ◽  
The Impact

Abstract The review will focus on the aspects of combinatorial chemistry and technologies that are more relevant in the modern pharmaceutical process. An historical, critical introduction is followed by three chapters, dealing with the use of combinatorial chemistry/high throughput synthesis in medicinal chemistry; the rational design of combinatorial libraries using computer-assisted combinatorial drug design; and the use of combinatorial technologies in biotechnology. The impact of “combinatorial thinking” in drug discovery in general, and in the examples reported in details, is critically discussed. Finally, an expert opinion on current and future trends in combinatorial chemistry and combinatorial technologies is provided.

Molecular Dynamics Simulations of Cluster-size Effect on Sputtering Process with Reactive Gas Cluster Ions

2005 ◽  
Vol 908 ◽  
Author(s):  
Takaaki Aoki ◽  
Jiro Matsuo
Keyword(s):  
Molecular Dynamics ◽  
Size Effect ◽  
Cluster Size ◽  
Incident Energy ◽  
Md Simulations ◽  
Cluster Ions ◽  
Experimental Conditions ◽  
Near Surface ◽  
The Impact ◽  
Reactive Cluster

AbstractTo investigate the size-effect of reactive clusters on sputtering processes, we performed molecular dynamics (MD) simulations of reactive cluster ions with various sizes impacting on solid targets. Various sizes of fluorine clusters, (F2)30, (F2)300 and (F2)3000, were irradiated on a Si(100) target at the same total incident energy of 6 keV. These clusters were irradiated on the same target one after another in order to reproduce real experimental conditions such as the accumulation of fluorine atoms in the target. The MD simulations of sequential cluster impacts enabled to perform various statistical analyses regarding the sputtered particles. The study of cluster size distributions showed that the sputtering process by reactive cluster ion impact has similarity with the emission from quasi-liquid materials excited to hyper-thermal conditions by ion bombardment. However, the major sputtered particles were different with each other; Si for (F2)30 (100 eV/atom), SiF2 for (F2)300 (10 eV/atom), and SiF3 for (F2)3000 (1 eV/atom). At the impact of a large size cluster with low incident energy, a large number of Si-F bondings were generated at the cluster-target interface surface, which enhances formation of volatile SiFx compounds with many fluorine atoms. In contrast, a small cluster with high kinetic energy-per-atom could cause the formation of numerous energetic surface atoms at the near surface region, which could be sputtered without being well fluoridated.

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