Abstract

The success of machine learning has been demonstrated time and time again in classification, generative modelling, and reinforcement learning. In particular, we have recently seen interesting developments where ML has been applied to the natural sciences (chemistry, physics, materials science, neuroscience and biology). Here, often the data is not abundant and very costly. This workshop will focus on the unique challenges of applying machine learning to molecules and materials.

Accurate prediction of chemical and physical properties is a crucial ingredient toward rational compound design in chemical and pharmaceutical industries. Many discoveries in chemistry can be guided by screening large databases of computational molecular structures and properties, but high level quantum-chemical calculations can take up to several days per molecule or material at the required accuracy, placing the ultimate achievement of in silico design out of reach for the foreseeable future. In large part the current state of the art for such problems is the expertise of individual researchers or at best highly-specific rule-based heuristic systems. Efficient methods in machine learning, applied to property and structure prediction, can therefore have pivotal impact in enabling chemical discovery and foster fundamental insights.

Because of this, in the past few years there has been a flurry of recent work towards designing machine learning techniques for molecule [1, 2, 4-11, 13-18, 20, 21, 23-32, 34-38] and material data [1-3, 5, 6, 12, 19, 24, 33]. These works have drawn inspiration from and made significant contributions to areas of machine learning as diverse as learning on graphs to models in natural language processing. Recent advances enabled the acceleration of molecular dynamics simulations, contributed to a better understanding of interactions within quantum many-body systems and increased the efficiency of density functional theory based quantum mechanical modeling methods. This young field offers unique opportunities for machine learning researchers and practitioners, as it presents a wide spectrum of challenges and open questions, including but not limited to representations of physical systems, physically constrained models, manifold learning, interpretability, model bias, and causality.

The goal of this workshop is to bring together researchers and industrial practitioners in the fields of computer science, chemistry, physics, materials science, and biology all working to innovate and apply machine learning to tackle the challenges involving molecules and materials. In a highly interactive format, we will outline the current frontiers and present emerging research directions. We aim to use this workshop as an opportunity to establish a common language between all communities, to actively discuss new research problems, and also to collect datasets by which novel machine learning models can be benchmarked. The program is a collection of invited talks, alongside contributed posters. A panel discussion will provide different perspectives and experiences of influential researchers from both fields and also engage open participant conversation. An expected outcome of this workshop is the interdisciplinary exchange of ideas and initiation of collaboration.

Accepted Papers

A Chemical Bond-based Representation of Materials [arXiv]

Van-Doan Nguyen, Le Dinh Khiet, Pham Tien Lam, Dam Hieu Chi

Spotlight Talk Automatically Extracting Action Graphs From Materials Science Synthesis Procedures [arXiv]

Sheshera Mysore, Edward Kim, Emma Strubell, Ao Liu, Haw-Shiuan Chang, Srikrishna Kompella, Kevin Huang, Andrew McCallum, Elsa Olivetti

Bayesian Protein Optimization

Stephan Eismann, Karen Sarkisyan, Stefano Ermon

Calibrated Boosting-forest [arXiv]

Haozhen Wu

Spotlight Talk ChemNet: A Transferable and Generalizable Deep Neural Network for Small-molecule Property Prediction

Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan Hodas

Constrained Bayesian Optimization for Automatic Chemical Design [arXiv]

Ryan-Rhys Griffiths, José Miguel Hernández-Lobato

Deep Learning for Prediction of Synergistic Effects of Anti-cancer Drugs

Kristina Preuer, Richard P.I. Lewis, Sepp Hochreiter, Andreas Bender, Krishna C. Bulusu, Günter Klambauer

Deep Learning Yields Virtual Assays

Thomas Unterthiner, Günter Klambauer, Andreas Mayr, Sepp Hochreiter

Spotlight Talk End-to-end Learning of Graph Neural Networks for Latent Molecular Representations

Masashi Tsubaki, Masashi Shimbo, Atsunori Kanemura, Hideki Asoh

Spotlight Talk “Found in translation”: Predicting Outcomes of Complex Organic Chemistry Reactions Using Neural Sequence-to-sequence Models [arXiv]

Philippe Schwaller, Théophile Gaudin, Dávid Lányi, Costas Bekas, Teodoro Laino

Spotlight Talk Learning a Generative Model for Validity in Complex Discrete Structures [arXiv]

David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato

Learning Hard Quantum Distributions With Variational Autoencoders [arXiv]

Andrea Rocchetto, Edward Grant, Sergii Strelchuk, Giuseppe Carleo, Simone Severini

Spotlight Talk Ligand Pose Optimization With Atomic Grid-based Convolutional Neural Networks [arXiv]

Matthew Ragoza, Lillian Turner, David Ryan Koes

Machine Learning-enabled Study of Proton Transfer Reaction Mechanisms on Titania Surfaces

Qian Yang, Muralikrishna Raju, Matthias Ihme, Evan J. Reed

Neural Network for Learning Universal Atomic Forces

Pham Tien Lam, Hiori Kinob, Takashi Miyakeb, Nguyen Viet Cuong, Dam Hieu Chia

Overcoming Data Scarcity With Transfer Learning [arXiv]

Maxwell L. Hutchinson, Erin Antono, Brenna M. Gibbons, Sean Paradiso, Julia Ling, Bryce Meredig

Pure Density Functional for Strong Correlations and the Thermodynamic Limit From Machine Learning

Li Li, Thomas E. Baker, Steven R. White, Kieron Burke

Spotlight Talk Semi-supervised Continuous Representation of Molecules [arXiv]

Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Timothy D. Hirzel, Jorge Aguilera-Iparraguirre, Ryan P. Adams, Alán Aspuru-Guzik

Semi-supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing [arXiv]

Hai Nguyen, Shin-ichi Maeda, Kenta Oono

Spotlight Talk Syntax-directed Variational Autoencoder for Molecule Generation [PDF]

Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song

Toxicity Prediction Using Self-normalizing Networks

Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter

Unsupervised Learning of Dynamical and Molecular Similarity Using Variance Minimization

Brooke E. Husic, Vijay S. Pande

Sponsors

---

References

[1]

Behler, J., Lorenz, S., Reuter, K. (2007). Representing molecule-surface interactions with symmetry-adapted neural networks. J. Chem. Phys., 127(1), 07B603.

[2]

Behler, J., Parrinello, M. (2007). Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett., 98(14), 146401.

[3]

Kang, B., Ceder, G. (2009). Battery materials for ultrafast charging and discharging. Nature, 458(7235), 190.

[4]

Bartók, A. P., Payne, M. C., Kondor, R., Csányi, G. (2010). Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett., 104(13), 136403.

[5]

Behler, J. (2011). Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys, 134(7), 074106.

[6]

Behler, J. (2011). Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys. Chem. Chem. Phys., 13(40), 17930-17955.

[7]

Rupp, M., Tkatchenko, A., Müller, K.-R., von Lilienfeld, O. A. (2012). Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett., 108(5), 058301.

[8]

Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R., Burke, K. (2012). Finding density functionals with machine learning. Phys. Rev. Lett., 108(25), 253002.

[9]

Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.-R., von Lilienfeld, O. A. (2013). Machine learning of molecular electronic properties in chemical compound space. New J. Phys., 15(9), 095003.

[10]

Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., Tkatchenko, A., Müller, K.-R. (2013). Assessment and validation of machine learning methods for predicting molecular atomization energies. J. Chem. Theory Comput., 9(8), 3404-3419.

[11]

Bartók, A. P., Kondor, R., Csányi, G. (2013). On representing chemical environments. Phys. Rev. B, 87(18), 184115.

[12]

Schütt K. T., Glawe, H., Brockherde F., Sanna A., Müller K.-R., Gross E. K. U. (2014). How to represent crystal structures for machine learning: towards fast prediction of electronic properties. Phys. Rev. B., 89(20), 205118.

[13]

Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., Pande, V. (2015). Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072.

[14]

Rupp, M., Ramakrishnan, R., & von Lilienfeld, O. A. (2015). Machine learning for quantum mechanical properties of atoms in molecules. J. Phys. Chem. Lett., 6(16), 3309-3313.

[15]

V. Botu, R. Ramprasad (2015). Learning scheme to predict atomic forces and accelerate materials simulations., Phys. Rev. B, 92(9), 094306.

[16]

Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., Tkatchenko, A. (2015). Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett, 6(12), 2326-2331.

[17]

Alipanahi, B., Delong, A., Weirauch, M. T., Frey, B. J. (2015). Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. ‎Nat. Biotechnol., 33(8), 831-838.

[18]

Duvenaud, D. K., Maclaurin, D., Aguilera-Iparraguirre, J., Gomez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., Adams, R. P. (2015). Convolutional networks on graphs for learning molecular fingerprints. NIPS, 2224-2232.

[19]

Faber F. A., Lindmaa A., von Lilienfeld, O. A., Armiento, R. (2016). Machine learning energies of 2 million elpasolite (A B C 2 D 6) crystals. Phys. Rev. Lett., 117(13), 135502.

[20]

Gomez-Bombarelli, R., Duvenaud, D., Hernandez-Lobato, J. M., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., Aspuru-Guzik, A. (2016). Automatic chemical design using a data-driven continuous representation of molecules. arXiv preprint arXiv:1610.02415.

[21]

Wei, J. N., Duvenaud, D, Aspuru-Guzik, A. (2016). Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci., 2(10), 725-732.

[22]

Sadowski, P., Fooshee, D., Subrahmanya, N., Baldi, P. (2016). Synergies between quantum mechanics and machine learning in reaction prediction. J. Chem. Inf. Model., 56(11), 2125-2128.

[23]

Lee, A. A., Brenner, M. P., Colwell L. J. (2016). Predicting protein-ligand affinity with a random matrix framework. Proc. Natl. Acad. Sci., 113(48), 13564-13569.

[24]

Behler, J. (2016). Perspective: Machine learning potentials for atomistic simulations. J. Chem. Phys., 145(17), 170901.

[25]

De, S., Bartók, A. P., Csányi, G., Ceriotti, M. (2016). Comparing molecules and solids across structural and alchemical space. Phys. Chem. Chem. Phys., 18(20), 13754-13769.

[26]

Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K.-R., Tkatchenko, A. (2017). Quantum-chemical insights from deep tensor neural networks. Nat. Commun., 8, 13890.

[27]

Segler, M. H., Waller, M. P. (2017). Neural‐symbolic machine learning for retrosynthesis and reaction prediction. ‎Chem. Eur. J., 23(25), 5966-5971.

[28]

Kusner, M. J., Paige, B., Hernández-Lobato, J. M. (2017). Grammar variational autoencoder. arXiv preprint arXiv:1703.01925.

[29]

Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H., Jensen K. F. (2017). Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci., 3(5), 434-443.

[30]

Altae-Tran, H., Ramsundar, B., Pappu, A. S., Pande, V. (2017). Low data drug discovery with one-shot learning. ACS Cent. Sci., 3(4), 283-293.

[31]

Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., Dahl, G. E. (2017). Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212.

[32]

Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, Igor, Schütt, K. T., Müller, K.-R. (2017). Machine learning of accurate energy-conserving molecular force fields. Sci. Adv., 3(5), e1603015.

[33]

Ju, S., Shiga T., Feng L., Hou Z., Tsuda, K., Shiomi J. (2017). Designing nanostructures for phonon transport via bayesian optimization. Phys. Rev. X, 7(2), 021024.

[34]

Ramakrishnan, R, von Lilienfeld, A. (2017). Machine learning, quantum chemistry, and chemical space. Reviews in Computational Chemistry, 225-256.

[35]

Hernandez-Lobato, J. M., Requeima, J., Pyzer-Knapp, E. O., Aspuru-Guzik, A. (2017). Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space. arXiv preprint arXiv:1706.01825.

[36]

Smith, J., Isayev, O., Roitberg, A. E. (2017). ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci., 8(4), 3192-3203.

[37]

Brockherde, F., Li, L., Burke, K., Müller, K.-R. By-passing the Kohn-Sham equations with machine learning. Nat. Commun., 8, 872.

[38]

Schütt, K. T., Kindermans, P. J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., Müller, K. R. (2017). MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS (accepted).

---

Contact: Please direct any questions to qm.nips2017@gmail.com.