computational ligand design; gradient based optimization; machine learning; chemical compound space; computational drug design; density functional theory; van der Waals interactions; rational compound design
Zaspel Peter, Huang Bing, Harbrecht Helmut, von Lilienfeld O. Anatole (2019), Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited, in Journal of Chemical Theory and Computation
, 15(3), 1546-1559.
Tahchieva Diana N., Bakowies Dirk, Ramakrishnan Raghunathan, von Lilienfeld O. Anatole (2018), Torsional Potentials of Glyoxal, Oxalyl Halides, and Their Thiocarbonyl Derivatives: Challenges for Popular Density Functional Approximations, in Journal of Chemical Theory and Computation
, 14(9), 4806-4817.
Chang K. Y. Samuel, von Lilienfeld O. Anatole (2018), AlxGa1−xAs crystals with direct 2 eV band gaps from computational alchemy, in Physical Review Materials
, 2(7), 073802-073802.
Faber Felix A., Christensen Anders S., Huang Bing, von Lilienfeld O. Anatole (2018), Alchemical and structural distribution based representation for universal quantum machine learning, in The Journal of Chemical Physics
, 148(24), 241717-241717.
Rupp Matthias, von Lilienfeld O. Anatole, Burke Kieron (2018), Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry, in The Journal of Chemical Physics
, 148(24), 241401-241401.
Bereau Tristan, DiStasio Robert A., Tkatchenko Alexandre, von Lilienfeld O. Anatole (2018), Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning, in The Journal of Chemical Physics
, 148(24), 241706-241706.
Kranz Julian J., Kubillus Maximilian, Ramakrishnan Raghunathan, von Lilienfeld O. Anatole, Elstner Marcus (2018), Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning, in Journal of Chemical Theory and Computation
, 14(5), 2341-2352.
Faber Felix A., Hutchison Luke, Huang Bing, Gilmer Justin, Schoenholz Samuel S., Dahl George E., Vinyals Oriol, Kearnes Steven, Riley Patrick F., von Lilienfeld O. Anatole (2017), Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error, in Journal of Chemical Theory and Computation
, 13(11), 5255-5264.
Browning Nicholas, Ramakrishnan Raghunathan, von Lilienfeld O. Anatole, Rothlisberger Ursula (2017), Genetic optimization of training sets for improved machine learning models of molecular properties, in J Phys Chem Lett
Ramakrishnan Raghunathan, von Lilienfeld O. Anatole (2017), Machine Learning, Quantum Chemistry, and Chemical Space, in Reviews in Computational Chemistry
, 30, 225.
Faber Felix A., Lindmaa Alexander, von Lilienfeld O. Anatole, Armiento Rickard (2016), Machine Learning Energies of 2 Million Elpasolite (ABC2D6) Crystals, in Physical Review Letters
, 117(13), 135502-135502.
Solovyeva Alisa, von Lilienfeld O. Anatole (2016), Alchemical screening of ionic crystals, in Phys Chem Chem Phys
, 18, 31078.
Chang K. Y. Samuel, Fias Stijn, Ramakrishnan Raghunathan, von Lilienfeld O. Anatole (2016), Fast and accurate predictions of covalent bonds in chemical space, in JOURNAL OF CHEMICAL PHYSICS
, 144(17), 174110.
Baben M. To, Achenbach J. O., von Lilienfeld O. A. (2016), Guiding ab initio calculations by alchemical derivatives, in JOURNAL OF CHEMICAL PHYSICS
, 144(10), 104103.
Gillingham Dennis, Geigle S, von Lilienfeld O. Anatole (2016), Properties and reactivity of nucleic acids relevant to epigenomics, transcriptomics, and therapeutics, in Chem. Soc. Rev.
Al-Hamdani Yasmine S., Alfe Dario, von Lilienfeld O. Anatole, Michaelides Angelos (2016), Tuning dissociation using isoelectronically doped graphene and hexagonal boron nitride: Water and other small molecules, in JOURNAL OF CHEMICAL PHYSICS
, 144(15), 154706.
Huang Bing, von Lilienfeld O. Anatole (2016), Understanding molecular representations in machine learning: The role of uniqueness and target similarity, in J Chem Phys
Ramakrishnan Raghunathan, Hartmann Mia, Tapavicza Enrico, von Lilienfeld O. Anatole (2015), Electronic spectra from TDDFT and machine learning in chemical space, in The Journal of Chemical Physics
, 143(8), 084111-084111.
Vu Kevin, Snyder John C., Li Li, Rupp Matthias, Chen Brandon F., Khelif Tarek, Müller Klaus-Robert, Burke Kieron (2015), Understanding kernel ridge regression: Common behaviors from simple functions to density functionals, in International Journal of Quantum Chemistry
, 115(16), 1115-1128.
Al-Hamdani Yasmine S., Ma Ming, Alfè Dario, von Lilienfeld O. Anatole, Michaelides Angelos (2015), Communication: Water on hexagonal boron nitride from diffusion Monte Carlo, in The Journal of Chemical Physics
, 142(18), 181101-181101.
Ramakrishnan Raghunathan, Dral Pavlo O., Rupp Matthias, von Lilienfeld O. Anatole (2015), Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach, in Journal of Chemical Theory and Computation
, 11(5), 2087-2096.
Ramakrishnan Raghunathan, Rauhut Guntram (2015), Semi-quartic force fields retrieved from multi-mode expansions: Accuracy, scaling behavior, and approximations, in The Journal of Chemical Physics
, 142(15), 154118-154118.
Raghunathan Ramakrishnan Pavlo O. Dral Matthias Rupp O. Anatole von Lilienfeld (2015), Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach, in Journal of Chemical Theory and Computation
Felix Faber Alexander Lindmaa O. Anatole von Lilienfeld Rickard Armiento (2015), Crystal structure representations for machine learning models of formation energies, in International Journal of Quantum Chemistry
O. Anatole von Lilienfeld Raghunathan Ramakrishnan Matthias Rupp Aaron Knoll (2015), Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties, in International Journal of Quantum Chemistry
Pavlo O. Dral O. Anatole von Lilienfeld Walter Thiel (2015), Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations, in Journal of Chemical Theory and Computation
Katja Hansen Franziska Biegler Raghunathan Ramakrishnan Wiktor Pronobis O. Anatole von Lilienfel (2015), Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space, in Journal of Chemical Physics Letters
Ramakrishnan Raghunathan von Lilienfeld O. Anatole (2015), Many Molecular Properties from One Kernel in Chemical Space, in CHIMIA
Tristan Bereau Denis Andrienko O. Anatole von Lilienfeld (2015), Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules, in Journal of Chemical Theory and Computation
Yasmine S. Al-Hamdani Ming Ma Dario Alfè O. Anatole von Lilienfeld Angelos Michaelides (2015), Water on hexagonal boron nitride from diffusion Monte Carlo, in Journal of Chemical Physics
Louis-François Arsenault Alejandro Lopez-Bezanilla O. Anatole von Lilienfeld and Andrew J. Millis (2014), Machine learning for many-body physics: The case of the Anderson impurity model, in Physical Review B
, 90, 155136.
Lopez-Bezanilla Alejandro, von Lilienfeld O. Anatole (2014), Modeling electronic quantum transport with machine learning, in PHYSICAL REVIEW B
, 89(23), 235411.
Ramakrishnan R., Dral P., Rupp M., von Liliefneld OA (2014), Quantum chemistry structures and properties of 134 kilo molecules, in Scientific Data
, 1, 140022.
Bereau Tristan, Von Lilienfeld O. Anatole (2014), Toward transferable interatomic van der Waals interactions without electrons: The role of multipole electrostatics and many-body dispersion, in Journal of Chemical Physics
, 141(3), 034101.
Yasmine S. Al-Hamdani Dario Alfè O. Anatole von Lilienfeld and Angelos Michaeledis (2014), Water on BN doped benzene: A hard test for exchange-correlation functionals and the impact of exact exchange on weak binding, in Journal of Chemical Physics
, 141, 18C530.
Benali Anouar, Shulenburger Luke, Romero Nichols A., Kim Jeongnim, von Lilienfeld O. Anatole, Application of Diffusion Monte Carlo to Materials Dominated by van der Waals Interactions, in Journal of Chemical Theory and Computation
, 0(0), 0.
R. Ramakrishnan M. Hartmann E. Tapavicza O. A. von Lilienfeld, Electronic Spectra from TDDFT and Machine Learning in Chemical Space, in Journal of Chemical Physics
M. Rupp R. Ramakrishnan O. A. von Lilienfeld, Machine Learning for Quantum Mechanical Properties of Atoms in Molecules, in Journal of Physical Chemistry Letters
Chang KYS, von Lilienfeld OA, Quantum Mechanical Treatment of Variable Molecular Composition: From ``Alchemical'' Changes of State Functions to Rational Compound Design, in CHIMIA
, 0(0), 0-0.
Atomistic details of matter determine physical and chemical properties. Much research has been carried out in order to understand and model this relationship using quantum mechanical (QM) first principles, statistical mechanics, and the ever increasing role of the computational sciences. Employed methods are based on a physico-chemical framework that nowadays permits to routinely compute relevant properties for any combinations of atomic configurations and composition, also called chemical compound space (CCS). The task of using QM for the discovery of novel compounds with improved properties is therefore equivalent to a combinatorial optimization problem in CCS. Tackling this task through QM based high-throughput screening, albeit straight-forward, is the least efficient way. The overall goal of this proposal is to make a comprehensive exploration of CCS possible by developing QM based rational compound design (QM-RCD) schemes that are drastically more efficient than screening. The proposed QM-RCD approach is based on two of the most fundamental and complementary strategies in numerical optimization theory, namely variational and correlational methods. For the former, differential, response-like, QM predictions of properties will be studied for compounds that are ``close'' in CCS. For the latter, intelligent data analysis methods (machine learning) will be applied to exploit complex correlations. Such models promise to dramatically accelerate the estimation of QM properties, enabling the modeling of massive numbers of compounds within discrete combinatorial optimization implementations, e.g. evolutionary or Monte Carlo based. While many QM properties are amenable to such efforts, the focus of this proposal lies on the archetypical problem of ligand design. The usefulness of this highly interdisciplinary endeavor, tightly linking theoretical physics with computational chemistry and biochemistry, and with computer sciences, will be exemplified for realistic and relevant ligand leads, such as DNA intercalating drugs (dominated by pi-pi-interactions) and ligand-protein (dominated by hydrogen bonding and polarization) interactions.