A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

Gastegger, Michael; McSloy, Adam; Luya, Michael; Schütt, Kristof T.; Maurer, Reinhard J.

doi:10.1063/5.0012911

Cited by 58 publications

(60 citation statements)

References 45 publications

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“…Promising models are multilayer energy-based fragment methods similar to ref ( 536 ) in combination with high-dimensional NNs 509 and density-like descriptors, 508 or automatically learned descriptors based on geometric information. 461 , 528 , 529 Especially, ML for wave functions like the SchNOrb model 77 , 80 could be helpful in this regard and are further interesting for dynamics simulations to compute wave function overlaps from ML. Further, it is important to encode the charge of the molecule in order to treat molecules of same composition, but different electronic charges.…”

Section: Data Sets For Excited Statesmentioning

confidence: 99%

“… 71 The fundamentals of quantum chemistry, e.g., to obtain an optimal solution to the Schrödinger equation or density functional theory, can be central ML applications. For the ground state, ML approximations to the molecular wave function 72 − 80 or the density (functional) of a system exist. 70 , 80 − 89 Obtaining a molecular wave function from ML can be seen as the most powerful approach in many perspectives, as any property we wish to know could be derived from it.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Electronically Excited States of Molecules

2020

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Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

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Section: Data Sets For Excited Statesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning for Electronically Excited States of Molecules

2020

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“…Thus, SchNOrb can help researchers in designing new pharmaceutical drugs. Moreover, molecular dynamics (MD) simulation analyzes how molecules behave and interact at an atomistic level [ 79 ]. In drug discovery, MD simulation is used to evaluate protein–ligand interactions and binding stability.…”

Section: Transforming Traditional Computational Drug Design Through Amentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

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Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

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“…18 In the past decade, researchers have developed a broad spectrum of different ML potentials. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] Recently, an ML-based model called Deep Potential -Smooth Edition (DeepPot-SE) 36 was developed to efficiently represent organic molecules, metals, semiconductors and insulators with an accuracy comparable to that of ab initio QM models. The DeepPot-SE model has recently been highlighted in simulations of interfacial processes in aqueous aerosol 37 and large-scale combustion reactions in the gas phase, 38 and demonstrated great success in providing predictive insight into complex reaction processes.…”

Section: Introductionmentioning

confidence: 99%

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

Zeng¹,

Giese²,

Ekesan³

et al. 2021

Preprint

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We develop a new Deep Potential - Range Correction (DPRc) machine learning potential for combined quantum mechanical/molecular mechanical (QM/MM) simulations of chemical reactions in the condensed phase. The new range correction enables short-ranged QM/MM interactions to be tuned for higher accuracy, and the correction smoothly vanishes within a specified cutoff. We further develop an active learning procedure for robust neural network training. We test the DPRc model and training procedure against a series of 6 non-enzymatic phosphoryl transfer reactions in solution that are important in mechanistic studies of RNA-cleaving enzymes. Specifically, we apply DPRc corrections to a base QM model and test its ability to reproduce free energy profiles generated from a target QM model. We perform comparisons using the MNDO/d and DFTB2 semiempirical models because they produce free energy profiles which differ significantly from each other, thereby providing us a rigorous stress test for the DPRc model and training procedure. The comparisons show that accurate reproduction of the free energy profiles requires correction of the QM/MM interactions out to 6 Å. We further find that the model's initial training benefits from generating data from temperature replica exchange simulations and including high-temperature configurations into the fitting procedure so the resulting models are trained to properly avoid high-energy regions. A single DPRc model was trained to reproduce 4 different reactions and yielded good agreement with the free energy profiles made from the target QM/MM simulations. The DPRc model was further demonstrated to be transferable to 2D free energy surfaces and 1D free energy profiles that were not explicitly considered in the training. Examination of the computational performance of the DPRc model showed that it was fairly slow when run on CPUs, but was sped up almost 100-fold when using an NVIDIA V100 GPUs, resulting in almost negligible overhead. The new DPRc model and training procedure provide a potentially powerful new tool for the creation of next-generation QM/MM potentials for a wide spectrum of free energy applications ranging from drug discovery to enzyme design.<br>

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A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

Abstract: How to cite:Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 58 publications

References 45 publications

Machine Learning for Electronically Excited States of Molecules

Machine Learning for Electronically Excited States of Molecules

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/molecular Mechanical Simulations of Chemical Reactions in Solution

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