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WIREs Comput Mol Sci

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ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data

Software Focus

Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, Ramachandran Subramanian, Bhargava U. Kota, Aditya Sonpal, Srirangaraj Setlur, Johannes Hachmann

Published Online: Jan 30 2020

DOI: 10.1002/wcms.1458

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Abstract ChemML is an open machine learning (ML) and informatics program suite that is designed to support and advance the data‐driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various data science tasks and execute ML workflows that are adapted specifically for the chemical and materials context. Key features are automation, general‐purpose utility, versatility, and user‐friendliness in order to make the application of modern data science a viable and widely accessible proposition in the broader chemistry and materials community. ChemML is also designed to facilitate methodological innovation, and it is one of the cornerstones of the software ecosystem for data‐driven in silico research. This article is categorized under: Software > Simulation Methods Computer and Information Science > Chemoinformatics Structure and Mechanism > Computational Materials Science Software > Molecular Modeling

The process of applying a model‐based active learning strategy to a pool of unlabeled candidates as implemented in ChemML. After initializing the training and test sets randomly, an ensemble of deep learning models score unlabeled candidates in a sequential or batch selection format to query one or several new data points that promise to improve the model the most

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Toy workflow/computation graph used in ChemML. (a) Plot of the computation graph with corresponding Python code (b) and input file (c); (d) shows the structure of a computation unit and (e) an example of its implementation in the Jupyter notebook graphical user interface. We stress that actual machine learning workflows are considerably more involved than this simplistic toy example

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Design scheme and architecture of the ChemML program package, consisting of ChemML Library and ChemML Wrapper. The overview shows the seven task classes and the methods available within them, as well as the six categories we distinguish with regards to the method sources

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ChemML provides a multitude of methods as part of seven core task classes to conduct data mining projects (such as the creation of data‐derived machine learning surrogate models)

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The process of applying transfer learning from a deep learning model 1 for target property α based on a large set of lower‐quality data to a more accurate model 2 based on a small set of high‐quality data

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References

Butler, KT, Davies, DW, Cartwright, H, Isayev, O, Walsh, A. Machine learning for molecular and materials science. Nature. 2018;559:547–555.
National Science and Technology Council. Materials genome initiative for global competitiveness, Technical report; 2011.
Hachmann, J, Windus, TL, McLean, JA, et al. Framing the role of big data and modern data science in chemistry. Technical report; 2018.
Haghighatlari, M, Hachmann, J. Advances of machine learning in molecular modeling and simulation. Curr Opin Chem Eng. 23:51–57.
Wang, H, Ji, Y, Li, Y. Simulation and design of energy materials accelerated by machine learning. WIREs Comput Mol Sci. 2019;e1421.
Hachmann, J, Afzal, MAF, Haghighatlari, M, Pal, Y. Building and deploying a cyberinfrastructure for the data‐driven design of chemical systems and the exploration of chemical space. Mol Simul. 2018;44:921–929. https://doi.org/10.1080/08927022.2018.1471692.
Afzal, MAF, Vishwakarma, G, Dudwadkar, JA, Haghighatlari, M, Hachmann, J. ChemLG – A program suite for the generation of compound libraries and the survey of chemical space; 2019. Available from https://github.com/hachmannlab/chemlg
Pal, Y, Evangelista, WS, Afzal,, MAF, Haghighatlari, M, Hachmann, J. ChemHTPS – An automated computational chemistry high‐throughput screening platform; 2019. Available from https://github.com/hachmannlab/chemhtps
Sonpal, A, Agrawal, S, Sivaraj, S, Hachmann, J. ChemBDDB – A big data database toolkit for chemical and materials data storage; 2019. Available from https://github.com/hachmannlab/chembddb
Haghighatlari, M, Hachmann, J. ChemML – A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data; 2019. Available from https://hachmannlab.github.io/chemml
Mitchell, JBO. Machine learning methods in chemoinformatics. WIREs Comput Mol Sci. 2014;4:468–481. https://doi.org/10.1002/wcms.1183.
Pedregosa, F, Varoquaux, G, Gramfort, A, et al. Scikit‐learn: Machine learning in python. J Mach Learn Res. 2011;12:2825–2830.
O`Boyle, NM, Banck, M, James, CA, Morley, C, Vandermeersch, T, Hutchison, GR. Open Babel: An open chemical toolbox. J Chem. 2011;3:33.
Landrum, G. RDKit: Open‐source cheminformatics. Availabe from http://www.rdkit.org
Ward, L, Agrawal, A, Choudhary, A, Wolverton, C. A general‐purpose machine learning framework for predicting properties of inorganic materials. NPJ Comput Mater. 2016;2:16028.
Rupp, M, Tkatchenko, A, Müller, K‐R, von Lilienfeld, OA. Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett. 2012;108:058301.
Hansen, K, Biegler, F, Ramakrishnan, R, et al. Machine learning predictions of molecular properties: Accurate many‐body potentials and nonlocality in chemical space. J Phys Chem Lett. 2015;6:2326–2331.
Duvenaud, D, Maclaurin, D, Aguilera‐Iparraguirre, J, et al. Convolutional networks on graphs for learning molecular fingerprints. Adv Neural Inf Proces Syst. 2015;2224–2232.
DRAGON (Software for Molecular Descriptor Calculation); 2011. Availabe from http://www.talete.mi.it/
Martin, A, Ashish, A, Paul, B, et al. TensorFlow: Large‐scale machine learning on heterogeneous systems; 2015. Available from https://www.tensorflow.org/
Chollet, F. Keras; 2015. Available from https://keras.io
Berthold, MR, Cebron, N, Fabian Dill, TR, et al. KNIME: The Konstanz information miner. In: Preisach, C, Burkhardt, H, Schmidt‐Thieme, L, Decker, R, editors. Data Analysis, Machine Learning and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008; p. 319–326.
Schütt, KT, Arbabzadah, F, Chmiela, S, Müller, K‐R, Tkatchenko, A. Quantum‐chemical insights from deep tensor neural networks. Nat Commun. 2017;8:13890.
Isayev, O, Oses, C, Toher, C, Gossett, E, Curtarolo, S, Tropsha, A. Universal fragment descriptors for predicting electronic properties of inorganic crystals. Nat Commun. 2017;8:15679.
Ferré, G, Haut, T, Barros, K. Learning molecular energies using localized graph kernels. J Chem Phys. 2017;146:114107.
Collins, CR, Gordon, GJ, von Lilienfeld, OA, Yaron, DJ. Constant size molecular descriptors for accurate machine learning models of molecular properties. J Chem Phys. 2017;148:241718.
Settle, B. Active learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan %26 Claypool, 2012; p. 1–114.
Cai, W, Zhang, M, Zhang, Y. Batch mode active learning for regression with expected model change. IEEE Trans Neural Netw Learn Syst. 2017;28:1668–1681.
Pan, SJ, Yang, Q. A survey on transfer learning. IEEE Trans Knowl Data Eng. 2010;22:1345–1359.
Vishwakarma, G. Machine learning model selection for predicting properties of high‐refractive‐index polymers [M.Sc. thesis]. University at Buffalo; 2018.
Afzal, MAF, Sonpal, A, Haghighatlari, M, Schultz, AJ, Hachmann, J. A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules. ChemRxiv. 2019;8217758.v1.
Afzal, MAF, Haghighatlari, M, Ganesh, SP, Cheng, C, Hachmann, J. Accelerated discovery of high‐refractive‐index polyimides via first‐principles molecular modeling, virtual high‐throughput screening, and data mining. J Phys Chem C. 2019;123:14610–14618.
Afzal, MAF, Cheng, C, Hachmann, J. Combining first‐principles and data modeling for the accurate prediction of the refractive index of organic polymers. J Chem Phys. 2018;148:241712.
Afzal, MAF, Hachmann, J. Benchmarking DFT approaches for the calculation of polarizability inputs for refractive index predictions in organic polymers. Phys Chem Chem Phys. 2019;21:4452–4460.
Afzal, MAF. From virtual high‐throughput screening and machine learning to the discovery and rational design of polymers for optical applications [Ph.D. thesis]. University at Buffalo; 2018.
Sonpal, A. Predicting melting points of deep eutectic solvents [M.Sc. thesis]. University at Buffalo; 2018.
Tian, Y. Inheritance of molecular orbital energies from monomer building blocks to larger copolymers in organic semiconductors [M.Sc. thesis]. University at Buffalo; 2016.
Shih, C‐Y. Systematic trends in results from different density functional theory models [M.Sc. thesis]. University at Buffalo; 2015.
Hachmann, J, Olivares‐Amaya, R, Atahan‐Evrenk, S, et al. The Harvard clean energy project: Large‐scale computational screening and design of organic photovoltaics on the world community grid. J Phys Chem Lett. 2011;2:2241–2251.
Olivares‐Amaya, R, Amador‐Bedolla, C, Hachmann, J, et al. Accelerated computational discovery of high‐performance materials for organic photovoltaics by means of cheminformatics. Energy Environ Sci. 2011;4:4849–4861.
Amador‐Bedolla, C, Olivares‐Amaya, R, Hachmann, J, Aspuru‐Guzik, A. Organic photovoltaics. In: Rajan, K, editor. Informatics for Materials Science and Engineering: Data‐driven Discovery for Accelerated Experimentation and Application. Oxford: Butterworth‐Heinemann, 2013; p. 423–442 [chapter 17].
Hachmann, J, Olivares‐Amaya, R, Jinich, A, et al. Lead candidates for high‐performance organic photovoltaics from high‐throughput quantum chemistry – The Harvard clean energy project. Energy Environ Sci. 2014;7:698–704.
Lopez, SA, Pyzer‐Knapp, EO, Simm, GN, et al. The Harvard organic photovoltaic dataset. Sci Data. 2016;3:160086.
Sanchez‐Lengeling, B, Aspuru‐Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science. 2018;361:360–365.
Duran‐Frigola, M, Fernández‐Torras, A, Bertoni, M, Aloy, P. Formatting biological big data for modern machine learning in drug discovery. WIREs Comput Mol Sci. 2018;e1408. https://doi.org/10.1002/wcms.1408.
Xue, D, Gong, Y, Yang, Z, et al. Advances and challenges in deep generative models for de novo molecule generation. WIREs Comput Mol Sci. 2019;9:e1395. https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcms.1395.
Jørgensen, PB, Schmidt, MN, Winther, O. Deep generative models for molecular science. Mol Inform. 2018;37:1700133. https://doi.org/10.1002/minf.201700133.
Haghighatlari, M. Making machine learning work in chemistry: Methodological innovation, software development, and application studies [Ph.D. thesis]. University at Buffalo; 2019.
Krylov, A, Windus, TL, Barnes, T, et al. Perspective: Computational chemistry software and its advancement as illustrated through three grand challenge cases for molecular science. J Chem Phys. 2018;149:180901.
Wilkins‐Diehr, N, Crawford, TD. NSF`s inaugural software institutes: The science gateways community institute and the molecular sciences software institute. Comput Sci Eng. 2018;20:26–38.

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