Advanced search
Publication Cover
Molecular Simulation Volume 44, 2018 - Issue 11: Machine Learning
Submit an article Journal homepage
1,714
Views
20
CrossRef citations to date
0
Altmetric
Articles

Recruiting machine learning methods for molecular simulations of proteins

Shriyaa MittalCenter for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.ORCID Iconhttp://orcid.org/0000-0003-3490-1969View further author information
ORCID Icon &
Diwakar ShuklaCenter for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.;Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.Correspondence[email protected]
ORCID Iconhttp://orcid.org/0000-0003-4079-5381View further author information
ORCID Icon
Pages 891-904 | Received 12 Oct 2017, Accepted 27 Feb 2018, Published online: 13 Mar 2018

References

  • Carlsson J , Coleman RG , Setola V , et al . Ligand discovery from a dopamine D3 receptor homology model and crystal structure. Nat Chem Biol. 2011;7:769–778.
  • Colas C , Grewer C , Otte NJ , et al . Ligand discovery for the alanine-serine-cysteine transporter (ASCT2, SLC1A5) from homology modeling and virtual screening. PLoS Comput Biol. 2015;11:e1004477.
  • Ung PMU , Song W , Cheng L , et al . Inhibitor discovery for the human GLUT1 from homology modeling and virtual screening. ACS Chem Biol. 2016;11:1908–1916.
  • Kim SK , Chen Y , Abrol R , et al . Activation mechanism of the G protein-coupled sweet receptor heterodimer with sweeteners and allosteric agonists. Proc Natl Acad Sci USA. 2017;114:2568–2573.
  • Phillips JC , Braun R , Wang W , et al . Scalable molecular dynamics with NAMD. J Comput Chem. 2005;26:1781–1802.
  • Case D , Babin V , Berryman J , et al . Amber 14; 2014.
  • Eastman P , Swails J , Chodera JD , et al . OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol. 2017;13:e1005659.
  • Hess B , Kutzner C , Van Der Spoel D , et al . GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput. 2008;4:435–447.
  • Kumar S , Rosenberg JM , Bouzida D , et al . The weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. J Comput Chem. 1992;13:1011–1021.
  • Schlitter J , Engels M , Krüger P . Targeted molecular dynamics: a new approach for searching pathways of conformational transitions. J Mol Graphics. 1994;12:84–89.
  • Izrailev S , Stepaniants S , Balsera M , et al . Molecular dynamics study of unbinding of the avidin-biotin complex. Biophys J. 1997;72:1568–1581.
  • Wu X , Wang S . Self-guided molecular dynamics simulation for efficient conformational search. J Phys Chem B. 1998;102:7238–7250.
  • Sugita Y , Okamoto Y . Replica-exchange molecular dynamics method for protein folding. Chem Phys Lett. 1999;314:141–151.
  • Laio A , Parrinello M . Escaping free-energy minima. Proc Natl Acad Sci USA. 2002;99:12562–12566.
  • Bolhuis PG , Chandler D , Dellago C , et al . Transition path sampling: throwing ropes over rough mountain passes, in the dark. Annu Rev Phys Chem. 2002;53:291–318.
  • Maragliano L , Vanden-Eijnden E . A temperature accelerated method for sampling free energy and determining reaction pathways in rare events simulations. Chem Phys Lett. 2006;426:168–175.
  • Hamelberg D , de Oliveira CAF , McCammon JA . Sampling of slow diffusive conformational transitions with accelerated molecular dynamics. J Chem Phys. 2007;127:155102.
  • Darve E , Rodríguez-Gómez D , Pohorille A . Adaptive biasing force method for scalar and vector free energy calculations. J Chem Phys. 2008;128:144120.
  • Vanden-Eijnden E , Venturoli M . Revisiting the finite temperature string method for the calculation of reaction tubes and free energies. J Chem Phys. 2009;130:194103.
  • Miao Y , McCammon JA . Unconstrained enhanced sampling for free energy calculations of biomolecules: a review. Mol Simul. 2016;42:1046–1055.
  • Marks DS , Colwell LJ , Sheridan R , et al . Protein 3D structure computed from evolutionary sequence variation. PLoS ONE. 2011;6:e28766.
  • Hopf TA , Colwell LJ , Sheridan R , et al . Three-dimensional structures of membrane proteins from genomic sequencing. Cell. 2012;149:1607–1621.
  • Marks DS , Hopf TA , Sander C . Protein structure prediction from sequence variation. Nat Biotechnol. 2012;30:1072–1080.
  • Ovchinnikov S , Kamisetty H , Baker D . Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information. Elife. 2014;3:e02030.
  • Hopf TA , Schärfe CP , Rodrigues JP , et al . Sequence co-evolution gives 3D contacts and structures of protein complexes. Elife. 2014;3:e03430.
  • Liu Y , Palmedo P , Ye Q , et al . Enhancing evolutionary couplings with deep convolutional neural networks. Cell Syst. 2017;6:65–74.
  • Tang Y , Huang YJ , Hopf TA , et al . Protein structure determination by combining sparse NMR data with evolutionary couplings. Nat Methods. 2015;12:751–754.
  • Mittal J , Best RB . Tackling force-field bias in protein folding simulations: folding of villin HP35 and pin WW domains in explicit water. Biophys J. 2010;99:L26–L28.
  • Piana S , Lindorff-Larsen K , Shaw DE . How robust are protein folding simulations with respect to force field parameterization? Biophys J. 2011;100:L47–L49.
  • Kulik HJ , Luehr N , Ufimtsev IS , et al . Ab initio quantum chemistry for protein structures. J Phys Chem B. 2012;116:12501–12509.
  • Piana S , Klepeis JL , Shaw DE . Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. Curr Opin Struc Biol. 2014;24:98–105.
  • Shaw DE , Deneroff MM , Dror RO , et al . Anton, a special-purpose machine for molecular dynamics simulation. Commun ACM. 2008;51:91–97.
  • Shaw DE , Grossman J , Bank JA , et al . Anton 2: Raising the bar for performance and programmability in a special-purpose molecular dynamics supercomputer. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE Press; 2014. p. 41–53.
  • Shirts M , Pande VS . Screen savers of the world unite!. Science. 2000;290:1903–1904.
  • Bode B , Butler M , Dunning T , et al . The Blue Waters super-system for super-science -- contemporary HPC architectures. In: Contemporary HPC architectures. Sitka Publications; 2012. ISBN: 978-1-4665-6835-8.
  • Allison JR . Using simulation to interpret experimental data in terms of protein conformational ensembles. Curr Opin Struc Biol. 2017;43:79–87.
  • Shukla D , Meng Y , Roux B , et al . Activation pathway of Src kinase reveals intermediate states as targets for drug design. Nat Commun. 2014;5:3397.
  • Lapidus LJ , Acharya S , Schwantes CR , et al . Complex pathways in folding of protein g explored by simulation and experiment. Biophys J. 2014;107:947–955.
  • Kohlhoff KJ , Shukla D , Lawrenz M , et al . Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways. Nat Chem. 2014;6:15–21.
  • Lawrenz M , Shukla D , Pande VS . Cloud computing approaches for prediction of ligand binding poses and pathways. Sci Rep. 2015;5:7918.
  • Feinberg EN , Farimani AB , Hernandez CX , et al . Kinetic machine learning unravels ligand-directed conformational change of μ opioid receptor. bioRxiv; 2017:170886.
  • Sultan MM , Denny RA , Unwalla R , et al . Millisecond dynamics of BTK reveal kinome-wide conformational plasticity within the apo kinase domain. Sci Rep. 2017;7:15604.
  • Pande VS , Beauchamp K , Bowman GR . Everything you wanted to know about markov state models but were afraid to ask. Methods. 2010;52:99–105.
  • Beauchamp KA , Ensign DL , Das R , et al . Quantitative comparison of villin headpiece subdomain simulations and triplet-triplet energy transfer experiments. Proc Natl Acad Sci USA. 2011;108:12734–12739.
  • Lane TJ , Bowman GR , Beauchamp K , et al . Markov state model reveals folding and functional dynamics in ultra-long MD trajectories. J Am Chem Soc. 2011;133:18413–18419.
  • Shukla D , Hernández CX , Weber JK , et al . Markov state models provide insights into dynamic modulation of protein function. Acc Chem Res. 2015;48:414–422.
  • Jolliffe IT . Principal component analysis and factor analysis. In: Principal component analysis. New York (NY): Springer; 1986. p. 115–128.
  • Abdi H , Williams LJ . Principal component analysis. Wires Comput Stat. 2010;2:433–459.
  • Sharpee T , Rust NC , Bialek W . Maximally informative dimensions: analyzing neural responses to natural signals. In: Advances in neural information processing systems; 2003. p. 277–284.
  • Ichiye T , Karplus M . Collective motions in proteins: a covariance analysis of atomic fluctuations in molecular dynamics and normal mode simulations. Proteins Struct Funct Bioinf. 1991;11:205–217.
  • Amadei A , Linssen ABM , Berendsen HJC . Essential dynamics of proteins. Proteins Struct Funct Bioinf. 1993;17:412–425.
  • Hayward S , Go N . Collective variable description of native protein dynamics. Annu Rev Phys Chem. 1995;46:223–250.
  • Kitao A , Go N . Investigating protein dynamics in collective coordinate space. Curr Opin Struct Biol. 1999;9:164–169.
  • Schwantes CR , Pande VS . Improvements in Markov state model construction reveal many non-native interactions in the folding of NTL9. J Chem Theory Comput. 2013;9:2000–2009.
  • Pérez-Hernández G , Paul F , Giorgino T , et al . Identification of slow molecular order parameters for Markov model construction. J Chem Phys. 2013;139:015102.
  • Molgedey L , Schuster HG . Separation of a mixture of independent signals using time delayed correlations. Phys Rev Lett. 1994;72:3634–3637.
  • Naritomi Y , Fuchigami S . Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: the case of domain motions. J Chem Phys. 2011;134:065101.
  • Shukla D , Peck A , Pande VS . Conformational heterogeneity of the calmodulin binding interface. Nat Commun. 2016;7:10910.
  • Schwantes CR , Shukla D , Pande VS . Markov state models and tICA reveal a nonnative folding nucleus in simulations of NuG2. Biophys J. 2016;110:1716–1719.
  • Mukherjee S , Pantelopulos GA , Voelz VA . Markov models of the apo-MDM2 lid region reveal diffuse yet two-state binding dynamics and receptor poses for computational docking. Sci Rep. 2016;6:31631.
  • Razavi AM , Khelashvili G , Weinstein H . A Markov state-based quantitative kinetic model of sodium release from the dopamine transporter. Sci Rep. 2017;7:40076.
  • Ryckbosch SM , Wender PA , Pande VS . Molecular dynamics simulations reveal ligand-controlled positioning of a peripheral protein complex in membranes. Nat Commun. 2017;8.
  • Abramyan AM , Stolzenberg S , Li Z , et al . The isomeric preference of an atypical dopamine transporter inhibitor contributes to its selection of the transporter conformation. ACS Chem Neurosci. 2017;8:1735–1746.
  • Beauchamp KA , Bowman GR , Lane TJ , et al . MSMBuilder2: modeling conformational dynamics on the picosecond to millisecond scale. J Chem Theory Comput. 2011;7:3412–3419.
  • Scherer MK , Trendelkamp-Schroer B , Paul F , et al . PyEMMA 2: a software package for estimation, validation, and analysis of Markov models. J Chem Theory Comput. 2015;11:5525–5542.
  • Hinton GE . Reducing the dimensionality of data with neural networks. Science. 2006;313:504–507.
  • Wang Y , Yao H , Zhao S . Auto-encoder based dimensionality reduction. Neurocomputing. 2016;184:232–242.
  • Doerr S , Ariz I , Harvey MJ , et al . Dimensionality reduction methods for molecular simulations. arXiv preprint arXiv:171010629; 2017.
  • Wehmeyer C , Noé F . Time-lagged autoencoders: deep learning of slow collective variables for molecular kinetics. arXiv preprint arXiv:171011239; 2017.
  • Hernández CX , Wayment-Steele HK , Sultan MM , et al . Variational encoding of complex dynamics. arXiv preprint arXiv:171108576; 2017.
  • Johnston C . Netflix never used its \$1 million algorithm due to engineering costs; 2012. Available from: https://arstechnica.com/gadgets/2012/04/netix-never-used-its-1-million-algorithm-dueto-engineering-costs/.
  • Sultan MM , Pande VS . tICA-metadynamics: accelerating metadynamics by using kinetically selected collective variables. J Chem Theory Comput. 2017;13:2440–2447.
  • Hofmann T , Schölkopf B , Smola AJ . Kernel methods in machine learning. Ann Stat. 2008:1171–1220.
  • Schwantes CR , Pande VS . Modeling molecular kinetics with tICA and the kernel trick. J Chem Theory Comput. 2015;11:600–608.
  • Harrigan MP , Pande VS . Landmark kernel tICA for conformational dynamics. bioRxiv; 2017.
  • Wu H , Paul F , Wehmeyer C , et al . Multiensemble Markov models of molecular thermodynamics and kinetics. Proc Natl Acad Sci USA. 2016;113:E3221–E3230.
  • Chen W , Ferguson AL . Molecular enhanced sampling with autoencoders: on-the-fly collective variable discovery and accelerated free energy landscape exploration. arXiv preprint arXiv:180100203; 2017.
  • Fowler DM , Fields S . Deep mutational scanning: a new style of protein science. Nat Methods. 2014;11:801–807.
  • Kitzman JO , Starita LM , Lo RS , et al . Massively parallel single-amino-acid mutagenesis. Nat Methods. 2015;12:203–206.
  • Romero PA , Tran TM , Abate AR . Dissecting enzyme function with microfluidic-based deep mutational scanning. Proc Natl Acad Sci USA. 2015;112:7159–7164.
  • Harris DT , Wang N , Riley TP , et al . Deep mutational scans as a guide to engineering high affinity T cell receptor interactions with peptide-bound major histocompatibility complex. J Biol Chem. 2016;291:24566–24578.
  • Wrenbeck EE , Faber MS , Whitehead TA . Deep sequencing methods for protein engineering and design. Curr Opin Struct Biol. 2017;45:36–44.
  • Mann M , Jensen ON . Proteomic analysis of post-translational modifications. Nat Biotechnol. 2003;21:255–261.
  • Witze ES , Old WM , Resing KA , et al . Mapping protein post-translational modifications with mass spectrometry. Nat Methods. 2007;4:798–806.
  • Grimsrud PA , Swaney DL , Wenger CD , et al . Phosphoproteomics for the masses. ACS Chem Biol. 2010;5:105–119.
  • Choudhary C , Mann M . Decoding signalling networks by mass spectrometry-based proteomics. Nat Rev Mol Cell Bio. 2010;11:427–439.
  • Olivas ES , Guerrero JDM , Sober MM , et al . Handbook of research on machine learning applications and trends: Algorithms, methods, and techniques. IGI Global; 2009.
  • Sultan MM , Pande VS . Transfer learning from Markov models leads to efficient sampling of related systems. J Phys Chem B. 2017. DOI:10.1021/acs.jpcb.7b06896
  • Sultan MM , Wayment-Steele HK , Pande VS . Transferable neural networks for enhanced sampling of protein dynamics. arXiv preprint arXiv:180100636; 2018.
  • Tenenbaum JB , De Silva V , Langford JC . A global geometric framework for nonlinear dimensionality reduction. Science. 2000;290:2319–2323.
  • Das P , Moll M , Stamati H , et al . Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction. Proc Natl Acad Sci USA. 2006;103:9885–9890.
  • Stamati H , Clementi C , Kavraki LE . Application of nonlinear dimensionality reduction to characterize the conformational landscape of small peptides. Proteins Struct Funct Bioinf. 2010;78:223–235.
  • Coifman RR , Lafon S , Lee AB , et al . Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc Natl Acad Sci USA. 2005;102:7426–7431.
  • Ferguson AL , Panagiotopoulos AZ , Debenedetti PG , et al . Systematic determination of order parameters for chain dynamics using diffusion maps. Proc Natl Acad Sci USA. 2010;107:13597–13602.
  • Moffett A , Shukla D . On the transferability of time-lagged independent components between similar molecular dynamics systems. arXiv preprint arXiv:1710.00443; 2017.
  • Segaran T . Programming collective intelligence: building smart web 2.0 applications. O’Reilly Media, Inc.; 2007.
  • Vanatta DK , Shukla D , Lawrenz M , et al . A network of molecular switches controls the activation of the two-component response regulator NtrC. Nat Commun. 2015;6:7283.
  • Moffett AS , Bender KW , Huber SC , et al . Molecular dynamics simulations reveal the conformational dynamics of Arabidopsis thaliana BRI1 and BAK1 receptor-like kinases. J Biol Chem. 2017;292:12643–12652.
  • Selvam B , Shukla D . Understanding the conformational diversity of proton-coupled oligopeptide transporter (POT) family. Biophys J. 2017;112:16a–17a.
  • Pontiggia F , Pachov D , Clarkson M , et al . Free energy landscape of activation in a signalling protein at atomic resolution. Nat Commun. 2015;6:7284.
  • Bowman GR , Ensign DL , Pande VS . Enhanced modeling via network theory: adaptive sampling of markov state models. J Chem Theory Comput. 2010;6:787–794.
  • Chiavazzo E , Covino R , Coifman RR , et al . Intrinsic map dynamics exploration for uncharted effective free-energy landscapes. Proc Natl Acad Sci USA. 2017;114:E5494–E5503.
  • Tiwary P , Berne BJ . Spectral gap optimization of order parameters for sampling complex molecular systems. Proc Natl Acad Sci USA. 2016;113:2839–2844.
  • Preto J , Clementi C . Fast recovery of free energy landscapes via diffusion-map-directed molecular dynamics. Phys Chem Chem Phys. 2014;16:19181–19191.
  • Zhang J , Chen M . Unfolding hidden barriers by active enhanced sampling. arXiv preprint arXiv:170507414; 2017.
  • Zimmerman MI , Bowman GR . FAST conformational searches by balancing exploration/exploitation trade-offs. J Chem Theory Comput. 2015;11:5747–5757.
  • Perez A , MacCallum JL , Dill KA . Accelerating molecular simulations of proteins using bayesian inference on weak information. Proc Natl Acad Sci USA. 2015;112:11846–11851.
  • Perez A , Morrone JA , Dill KA . Accelerating physical simulations of proteins by leveraging external knowledge. Wires Comput Mol Sci. 2017;7:e1309.
  • Meng Y , Roux B . Locking the active conformation of c-src kinase through the phosphorylation of the activation loop. J Mol Biol. 2014;426:423–435.
  • Meng Y , Shukla D , Pande VS , et al . Transition path theory analysis of c-src kinase activation. Proc Natl Acad Sci USA. 2016;113:9193–9198.
  • Fajer M , Meng Y , Roux B . The activation of c-Src tyrosine kinase: conformational transition pathway and free energy landscape. J Phys Chem B. 2016;121:3352–3363.
  • Shamsi Z , Cheng K , Shukla D . REinforcement learning based Adaptive samPling: REAPing rewards by exploring protein conformational landscapes. arXiv; 2017.
  • Sutton RS , Barto AG . Reinforcement learning: an introduction. Vol. 1. Cambridge: MIT Press; 1998.
  • Silver D , Huang A , Maddison CJ , et al . Mastering the game of Go with deep neural networks and tree search. Nature. 2016;529:484–489.
  • Shamsi Z , Moffett AS , Shukla D . Enhanced unbiased sampling of protein dynamics using evolutionary coupling information. Sci Rep. 2017;7:12700.
  • Feng J , Shukla D . Characterizing conformational dynamics of proteins using evolutionary couplings. J Phys Chem B. 2018;122:1017–1025.
  • Asamitsu K , Hirokawa T , Hibi Y , et al . Molecular dynamics simulation and experimental verification of the interaction between cyclin T1 and HIV-1 Tat proteins. PLoS ONE. 2015;10:e0119451.
  • Dror RO , Mildorf TJ , Hilger D , et al . Structural basis for nucleotide exchange in heterotrimeric G proteins. Science. 2015;348:1361–1365.
  • Latorraca NR , Fastman NM , Venkatakrishnan A , et al . Mechanism of substrate translocation in an alternating access transporter. Cell. 2017;169:96–107.
  • Pan AC , Xu H , Palpant T , et al . Quantitative characterization of the binding and unbinding of millimolar drug fragments with molecular dynamics simulations. J Chem Theory Comput. 2017;13:3372–3377.
  • Gregorio GG , Masureel M , Hilger D , et al . Single-molecule analysis of ligand efficacy in β2AR--G-protein activation. Nature. 2017;547:68–73.
  • Lindorff-Larsen K , Maragakis P , Piana S , et al . Systematic validation of protein force fields against experimental data. PLoS ONE. 2012;7:e32131.
  • Huang J , MacKerell AD . CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data. J Comput Chem. 2013;34:2135–2145.
  • Islam SM , Stein RA , Mchaourab HS , et al . Structural refinement from restrained-ensemble simulations based on EPR/DEER data: application to T4 lysozyme. J Phys Chem B. 2013;117:4740–4754.
  • Roux B , Islam SM . Restrained-ensemble molecular dynamics simulations based on distance histograms from double electron-electron resonance spectroscopy. J Phys Chem B. 2013;117:4733–4739.
  • Mittal S , Shukla D . Predicting optimal DEER label positions to study protein conformational heterogeneity. J Phys Chem B. 2017;121:9761–9770.
  • Islam SM , Roux B . Simulating the distance distribution between spin-labels attached to proteins. J Phys Chem B. 2015;119:3901–3911.
  • Noé F , Nüske F . A variational approach to modeling slow processes in stochastic dynamical systems. Multiscale Model Sim. 2013;11:635–655.
  • Nüske F , Keller BG , Pérez-Hernández G , et al . Variational approach to molecular kinetics. J Chem Theory Comput. 2014;10:1739–1752.
  • McGibbon RT , Hernández CX , Harrigan MP , et al . Osprey: hyperparameter optimization for machine learning. J Open Source Software. 2016;1.
  • Dror RO , Arlow DH , Maragakis P , et al . Activation mechanism of the β2-adrenergic receptor. Proc Natl Acad Sci USA. 2011;108:18684–18689.
  • Manglik A , Kim TH , Masureel M , et al . Structural insights into the dynamic process of β2-adrenergic receptor signaling. Cell. 2015;161:1101–1111.
  • Fowler PW , Orwick-Rydmark M , Radestock S , et al . Gating topology of the proton-coupled oligopeptide symporters. Structure. 2015;23:290–301.
  • Liu Y , Racah E , Correa J , et al . Application of deep convolutional neural networks for detecting extreme weather in climate datasets. arXiv preprint arXiv:160501156; 2016.
  • Jones N . How machine learning could help to improve climate forecasts. Nature. 2017;548:379–380.
  • Nosengo N . Can artificial intelligence create the next wonder material? Nature. 2016;533:22–25.
  • Zhang L , Tan J , Han D , et al . From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov Today. 2017;22:1680–1685.
  • Schneider G . Automating drug discovery. Nat Rev Drug Discov. 2017;17:97–113.
  • Stephens ZD , Lee SY , Faghri F , et al . Big data: astronomical or genomical? PLoS Biol. 2015;13:e1002195.
  • Vinyals O , Blundell C , Lillicrap T , et al . Matching networks for one shot learning. In: Advances in neural information processing systems; 2016. p. 3630–3638.
  • Ravi S , Larochelle H . Optimization as a model for few-shot learning. ICLR; 2016.
  • Xian Y , Schiele B , Akata Z . Zero-shot learning-the good, the bad and the ugly. arXiv; 2017.
  • McGibbon RT , Taube AG , Donchev AG , et al . Improving the accuracy of Møller-plesset perturbation theory with neural networks. J Chem Phys. 2017;147:161725.
  • McSkimming DI , Rasheed K , Kannan N . Classifying kinase conformations using a machine learning approach. BMC Bioinform. 2017;18:86.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.