1
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Eastman P, Pritchard BP, Chodera JD, Markland TE. Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning. J Chem Theory Comput 2024; 20:8583-8593. [PMID: 39318326 DOI: 10.1021/acs.jctc.4c00794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original data set by adding much more sampling of chemical space and more data on noncovalent interactions. We train a set of potential energy functions called Nutmeg on it. They are based on the TensorNet architecture. They use a novel mechanism to improve performance on charged and polar molecules, injecting precomputed partial charges into the model to provide a reference for the large-scale charge distribution. Evaluation of the new models shows that they do an excellent job of reproducing energy differences between conformations even on highly charged molecules or ones that are significantly larger than the molecules in the training set. They also produce stable molecular dynamics trajectories and are fast enough to be useful for routine simulation of small molecules.
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Affiliation(s)
- Peter Eastman
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
| | - Benjamin P Pritchard
- Molecular Sciences Software Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24060, United States
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Thomas E Markland
- Department of Chemistry, Stanford University, Stanford, California 94305, United States
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2
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Öztürk I, Gervasoni S, Guccione C, Bosin A, Vargiu AV, Ruggerone P, Malloci G. Force Fields, Quantum-Mechanical- and Molecular-Dynamics-Based Descriptors of Radiometal-Chelator Complexes. Molecules 2024; 29:4416. [PMID: 39339411 PMCID: PMC11434398 DOI: 10.3390/molecules29184416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Revised: 08/30/2024] [Accepted: 09/13/2024] [Indexed: 09/30/2024] Open
Abstract
Radiopharmaceuticals are currently a key tool in cancer diagnosis and therapy. Metal-based radiopharmaceuticals are characterized by a radiometal-chelator moiety linked to a bio-vector that binds the biological target (e.g., a protein overexpressed in a particular tumor). The right match between radiometal and chelator influences the stability of the complex and the drug's efficacy. Therefore, the coupling of the radioactive element to the correct chelator requires consideration of several features of the radiometal, such as its oxidation state, ionic radius, and coordination geometry. In this work, we systematically investigated about 120 radiometal-chelator complexes taken from the Cambridge Structural Database. We considered 25 radiometals and about 30 chelators, featuring both cyclic and acyclic geometries. We used quantum mechanics methods at the density functional theoretical level to generate the general AMBER force field parameters and to perform 1 µs-long all-atom molecular dynamics simulations in explicit water solution. From these calculations, we extracted several key molecular descriptors accounting for both electronic- and dynamical-based properties. The whole workflow was carefully validated, and selected test-cases were investigated in detail. Molecular descriptors and force field parameters for the complexes considered in this study are made freely available, thus enabling their use in predictive models, molecular modelling, and molecular dynamics investigations of the interaction of compounds with macromolecular targets. Our work provides new insights in understanding the properties of radiometal-chelator complexes, with a direct impact for rational drug design of this important class of drugs.
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Affiliation(s)
| | - Silvia Gervasoni
- Department of Physics, University of Cagliari, I-09042 Monserrato (CA), Italy; (I.Ö.); (C.G.); (A.B.); (A.V.V.); (P.R.)
| | | | | | | | | | - Giuliano Malloci
- Department of Physics, University of Cagliari, I-09042 Monserrato (CA), Italy; (I.Ö.); (C.G.); (A.B.); (A.V.V.); (P.R.)
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3
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Meza-González B, Ramírez-Palma DI, Carpio-Martínez P, Vázquez-Cuevas D, Martínez-Mayorga K, Cortés-Guzmán F. Quantum Topological Atomic Properties of 44K molecules. Sci Data 2024; 11:945. [PMID: 39209874 PMCID: PMC11362522 DOI: 10.1038/s41597-024-03723-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
We present a data set of quantum topological properties of atoms of 44K randomly selected molecules from the GDB-9 data set. These atomic properties were obtained as defined by the quantum theory of atoms in molecules (QTAIM) within an atomic basin, a region of real space bounded by zero-flux surfaces in the electron density gradient vector field. The wave function files were generated through DFT static calculations (B3LYP/6-31G), and the atomic properties were calculated using QTAIM. The calculated atomic properties include the energy of the atomic basin, the electronic population, the magnitude of the total dipole moment, and the magnitude of the total quadrupole moment. The atomic properties allow one to understand the chemical structure, reactivity, and molecular recognition. They can be incorporated into force fields for molecular dynamics or for predicting reactive sites. We believe that this data set could facilitate new studies in chemical informatics, machine learning applied to chemistry, and computational molecular design.
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Affiliation(s)
- Brandon Meza-González
- Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de Méxinclude thexico, Mexico City, Mexico
| | - David I Ramírez-Palma
- Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Mérida, Yucatán, Mexico
| | - Pablo Carpio-Martínez
- Centro Conjunto de Investigación en Química Sustentable UAEM-UNAM, Carretera Toluca-Atlacomulco, km. 14.5, Toluca, Estado de México, C.P. 50200, Mexico
| | - David Vázquez-Cuevas
- Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Mérida, Yucatán, Mexico
| | - Karina Martínez-Mayorga
- Instituto de Química, Unidad Mérida, Universidad Nacional Autónoma de México, Mérida, Yucatán, Mexico
| | - Fernando Cortés-Guzmán
- Facultad de Química, Universidad Nacional Autónoma de México, Ciudad de Méxinclude thexico, Mexico City, Mexico.
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4
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Zhu Y, Li M, Xu C, Lan Z. Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules. Sci Data 2024; 11:948. [PMID: 39209851 PMCID: PMC11362161 DOI: 10.1038/s41597-024-03788-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 08/15/2024] [Indexed: 09/04/2024] Open
Abstract
Due to rapid advancements in deep learning techniques, the demand for large-volume high-quality datasets grows significantly in chemical research. We developed a quantum-chemistry database that includes 443,106 small organic molecules with sizes up to 10 heavy atoms including C, N, O, and F. Ground-state geometry optimizations and frequency calculations of all compounds were performed at the B3LYP/6-31G* level with the BJD3 dispersion correction, while the excited-state single-point calculations were conducted at the ωB97X-D/6-31G* level. Totally twenty-seven molecular properties, such as geometric, thermodynamic, electronic and energetic properties, were gathered from these calculations. Meanwhile, we also established a comprehensive protocol for the construction of a high-volume quantum-chemistry dataset. Our QCDGE (Quantum Chemistry Dataset with Ground- and Excited-State Properties) dataset contains a substantial volume of data, exhibits high chemical diversity, and most importantly includes excited-state information. This dataset, along with its construction protocol, is expected to have a significant impact on the broad applications of machine learning studies across different fields of chemistry, especially in the area of excited-state research.
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Affiliation(s)
- Yifei Zhu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, 510006, P. R. China
- School of Environment, South China Normal University, Guangzhou, 510006, P. R. China
| | - Mengge Li
- School of Environment, South China Normal University, Guangzhou, 510006, P. R. China
| | - Chao Xu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, 510006, P. R. China
- School of Environment, South China Normal University, Guangzhou, 510006, P. R. China
| | - Zhenggang Lan
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, MOE Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, 510006, P. R. China.
- School of Environment, South China Normal University, Guangzhou, 510006, P. R. China.
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5
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Masuda K, Abdullah AA, Pflughaupt P, Sahakyan AB. Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning. Sci Data 2024; 11:911. [PMID: 39174574 PMCID: PMC11341866 DOI: 10.1038/s41597-024-03772-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 08/13/2024] [Indexed: 08/24/2024] Open
Abstract
We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These models, however, treat the DNA as a mere collection of four, A, T, G and C, letters, dismissing the past advancements in science that can enable the use of more intricate information from nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical (QM) and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming QM methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A->C mutation rates.
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Affiliation(s)
- Kairi Masuda
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Adib A Abdullah
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Patrick Pflughaupt
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK
| | - Aleksandr B Sahakyan
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, OX3 9DS, UK.
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6
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Behara PK, Jang H, Horton JT, Gokey T, Dotson DL, Boothroyd S, Bayly CI, Cole DJ, Wang LP, Mobley DL. Benchmarking Quantum Mechanical Levels of Theory for Valence Parametrization in Force Fields. J Phys Chem B 2024; 128:7888-7902. [PMID: 39087913 PMCID: PMC11331531 DOI: 10.1021/acs.jpcb.4c03167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/09/2024] [Accepted: 07/15/2024] [Indexed: 08/02/2024]
Abstract
A wide range of density functional methods and basis sets are available to derive the electronic structure and properties of molecules. Quantum mechanical calculations are too computationally intensive for routine simulation of molecules in the condensed phase, prompting the development of computationally efficient force fields based on quantum mechanical data. Parametrizing general force fields, which cover a vast chemical space, necessitates the generation of sizable quantum mechanical data sets with optimized geometries and torsion scans. To achieve this efficiently, choosing a quantum mechanical method that balances computational cost and accuracy is crucial. In this study, we seek to assess the accuracy of quantum mechanical theory for specific properties such as conformer energies and torsion energetics. To comprehensively evaluate various methods, we focus on a representative set of 59 diverse small molecules, comparing approximately 25 combinations of functional and basis sets against the reference level coupled cluster calculations at the complete basis set limit.
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Affiliation(s)
- Pavan Kumar Behara
- Center
for Neurotherapeutics, University of California, Irvine, California 92697, United States
| | - Hyesu Jang
- Chemistry
Department, University of California at
Davis, Davis, California 95616, United States
- OpenEye
Scientific Software, Santa
Fe, New Mexico 87508, United States
| | - Joshua T. Horton
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, U.K.
| | - Trevor Gokey
- Department
of Chemistry, University of California, Irvine, California 92697, United States
| | - David L. Dotson
- The
Open Force Field Initiative, Open Molecular Software Foundation, Davis, California 95616, United States
- Datryllic
LLC, Phoenix, Arizona 85003, United States
| | | | | | - Daniel J. Cole
- School
of Natural and Environmental Sciences, Newcastle
University, Newcastle
upon Tyne NE1 7RU, U.K.
| | - Lee-Ping Wang
- Chemistry
Department, University of California at
Davis, Davis, California 95616, United States
| | - David L. Mobley
- Center
for Neurotherapeutics, University of California, Irvine, California 92697, United States
- Department
of Chemistry, University of California, Irvine, California 92697, United States
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7
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Ginex T, Vázquez J, Estarellas C, Luque FJ. Quantum mechanical-based strategies in drug discovery: Finding the pace to new challenges in drug design. Curr Opin Struct Biol 2024; 87:102870. [PMID: 38914031 DOI: 10.1016/j.sbi.2024.102870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 06/02/2024] [Accepted: 06/04/2024] [Indexed: 06/26/2024]
Abstract
The expansion of the chemical space to tangible libraries containing billions of synthesizable molecules opens exciting opportunities for drug discovery, but also challenges the power of computer-aided drug design to prioritize the best candidates. This directly hits quantum mechanics (QM) methods, which provide chemically accurate properties, but subject to small-sized systems. Preserving accuracy while optimizing the computational cost is at the heart of many efforts to develop high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM-tailored physics-based force fields and the coupling of QM with machine learning, in conjunction with the computing performance of supercomputing resources, will enhance the ability to use these methods in drug discovery. The challenge is formidable, but we will undoubtedly see impressive advances that will define a new era.
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Affiliation(s)
- Tiziana Ginex
- Pharmacelera, Parc Científic de Barcelona (PCB), Baldiri Reixac 4-8, 08028 Barcelona, Spain
| | - Javier Vázquez
- Pharmacelera, Parc Científic de Barcelona (PCB), Baldiri Reixac 4-8, 08028 Barcelona, Spain; Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain
| | - Carolina Estarellas
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Química Teòrica i Computacional (IQTCUB), 08921 Santa Coloma de Gramenet, Spain
| | - F Javier Luque
- Departament de Nutrició, Ciències de l'Alimentació i Gastronomia, Universitat de Barcelona, Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Biomedicina (IBUB), 08921 Santa Coloma de Gramenet, Spain; Institut de Química Teòrica i Computacional (IQTCUB), 08921 Santa Coloma de Gramenet, Spain.
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8
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Miao R, Liu D, Mao L, Chen X, Zhang L, Yuan Z, Shi S, Li H, Li S. GR-pKa: a message-passing neural network with retention mechanism for pKa prediction. Brief Bioinform 2024; 25:bbae408. [PMID: 39171986 PMCID: PMC11339865 DOI: 10.1093/bib/bbae408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 07/26/2024] [Accepted: 08/01/2024] [Indexed: 08/23/2024] Open
Abstract
During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and biological activity. However, the experimental determination of pKa values is often laborious and complex. Moreover, existing prediction methods exhibit limitations in both the quantity and quality of the training data, as well as in their capacity to handle the complex structural and physicochemical properties of compounds, consequently impeding accuracy and generalization. Therefore, developing a method that can quickly and accurately predict molecular pKa values will to some extent help the structural modification of molecules, and thus assist the development process of new drugs. In this study, we developed a cutting-edge pKa prediction model named GR-pKa (Graph Retention pKa), leveraging a message-passing neural network and employing a multi-fidelity learning strategy to accurately predict molecular pKa values. The GR-pKa model incorporates five quantum mechanical properties related to molecular thermodynamics and dynamics as key features to characterize molecules. Notably, we originally introduced the novel retention mechanism into the message-passing phase, which significantly improves the model's ability to capture and update molecular information. Our GR-pKa model outperforms several state-of-the-art models in predicting macro-pKa values, achieving impressive results with a low mean absolute error of 0.490 and root mean square error of 0.588, and a high R2 of 0.937 on the SAMPL7 dataset.
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Affiliation(s)
- Runyu Miao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China
| | - Danlin Liu
- Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, No. 3663, Zhongshan North Road, Putuo District, Shanghai, 200062, China
- School of Computer Science and Technology, East China Normal University, No. 3663, Zhongshan North Road, Putuo District, Shanghai, 200062, China
| | - Liyun Mao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China
| | - Xingyu Chen
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China
| | - Leihao Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China
| | - Zhen Yuan
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China
| | - Shanshan Shi
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China
- Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, No. 3663, Zhongshan North Road, Putuo District, Shanghai, 200062, China
- Lingang Laboratory, No. 319, Yueyang Road, Xuhui District, Shanghai, 200031, China
| | - Shiliang Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, No. 130, Meilong Road, Xuhui District, Shanghai, 200237, China
- Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, No. 3663, Zhongshan North Road, Putuo District, Shanghai, 200062, China
- Department of Pain management, HuaDong Hospital affiliated to Fudan University, No. 221, West Yan'an Road, Jing'an District, Shanghai, 200040, China
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9
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Katzberger P, Riniker S. A general graph neural network based implicit solvation model for organic molecules in water. Chem Sci 2024; 15:10794-10802. [PMID: 39027274 PMCID: PMC11253111 DOI: 10.1039/d4sc02432j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 05/24/2024] [Indexed: 07/20/2024] Open
Abstract
The dynamical behavior of small molecules in their environment can be studied with classical molecular dynamics (MD) simulations to gain deeper insight on an atomic level and thus complement and rationalize the interpretation of experimental findings. Such approaches are of great value in various areas of research, e.g., in the development of new therapeutics. The accurate description of solvation effects in such simulations is thereby key and has in consequence been an active field of research since the introduction of MD. So far, the most accurate approaches involve computationally expensive explicit solvent simulations, while widely applied models using an implicit solvent description suffer from reduced accuracy. Recently, machine learning (ML) approaches that provide a probabilistic representation of solvation effects have been proposed as potential alternatives. However, the associated computational costs and minimal or lack of transferability render them unusable in practice. Here, we report the first example of a transferable ML-based implicit solvent model trained on a diverse set of 3 000 000 molecular structures that can be applied to organic small molecules for simulations in water. Extensive testing against reference calculations demonstrated that the model delivers on par accuracy with explicit solvent simulations while providing an up to 18-fold increase in sampling rate.
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Affiliation(s)
- Paul Katzberger
- Department of Chemistry and Applied Biosciences, ETH Zürich Vladimir-Prelog-Weg 2 8093 Zürich Switzerland
| | - Sereina Riniker
- Department of Chemistry and Applied Biosciences, ETH Zürich Vladimir-Prelog-Weg 2 8093 Zürich Switzerland
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10
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Atz K, Nippa DF, Müller AT, Jost V, Anelli A, Reutlinger M, Kramer C, Martin RE, Grether U, Schneider G, Wuitschik G. Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry. RSC Med Chem 2024; 15:2310-2321. [PMID: 39026644 PMCID: PMC11253849 DOI: 10.1039/d4md00196f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 05/25/2024] [Indexed: 07/20/2024] Open
Abstract
Suzuki cross-coupling reactions are considered a valuable tool for constructing carbon-carbon bonds in small molecule drug discovery. However, the synthesis of chemical matter often represents a time-consuming and labour-intensive bottleneck. We demonstrate how machine learning methods trained on high-throughput experimentation (HTE) data can be leveraged to enable fast reaction condition selection for novel coupling partners. We show that the trained models support chemists in determining suitable catalyst-solvent-base combinations for individual transformations including an evaluation of the need for HTE screening. We introduce an algorithm for designing 96-well plates optimized towards reaction yields and discuss the model performance of zero- and few-shot machine learning. The best-performing machine learning model achieved a three-category classification accuracy of 76.3% (±0.2%) and an F 1-score for a binary classification of 79.1% (±0.9%). Validation on eight reactions revealed a receiver operating characteristic (ROC) curve (AUC) value of 0.82 (±0.07) for few-shot machine learning. On the other hand, zero-shot machine learning models achieved a mean ROC-AUC value of 0.63 (±0.16). This study positively advocates the application of few-shot machine learning-guided reaction condition selection for HTE campaigns in medicinal chemistry and highlights practical applications as well as challenges associated with zero-shot machine learning.
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Affiliation(s)
- Kenneth Atz
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Alex T Müller
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Vera Jost
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Andrea Anelli
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Michael Reutlinger
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Christian Kramer
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Rainer E Martin
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
| | - Georg Wuitschik
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd. Grenzacherstrasse 124 4070 Basel Switzerland
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11
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Kim J, Chang W, Ji H, Joung I. Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction. J Chem Inf Model 2024; 64:5028-5040. [PMID: 38916580 DOI: 10.1021/acs.jcim.4c00772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
We examined pretraining tasks leveraging abundant labeled data to effectively enhance molecular representation learning in downstream tasks, specifically emphasizing graph transformers to improve the prediction of ADMET properties. Our investigation revealed limitations in previous pretraining tasks and identified more meaningful training targets, ranging from 2D molecular descriptors to extensive quantum chemistry simulations. These data were seamlessly integrated into supervised pretraining tasks. The implementation of our pretraining strategy and multitask learning outperforms conventional methods, achieving state-of-the-art outcomes in 7 out of 22 ADMET tasks within the Therapeutics Data Commons by utilizing a shared encoder across all tasks. Our approach underscores the effectiveness of learning molecular representations and highlights the potential for scalability when leveraging extensive data sets, marking a significant advancement in this domain.
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Affiliation(s)
- Jungwoo Kim
- Standigm Inc., 182 Dogok-ro, 6F, Gangnam-gu, Seoul 06261, Korea
| | - Woojae Chang
- Standigm Inc., 182 Dogok-ro, 6F, Gangnam-gu, Seoul 06261, Korea
| | - Hyunjun Ji
- Standigm Inc., 182 Dogok-ro, 6F, Gangnam-gu, Seoul 06261, Korea
| | - InSuk Joung
- Standigm Inc., 182 Dogok-ro, 6F, Gangnam-gu, Seoul 06261, Korea
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12
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Medrano Sandonas L, Van Rompaey D, Fallani A, Hilfiker M, Hahn D, Perez-Benito L, Verhoeven J, Tresadern G, Kurt Wegner J, Ceulemans H, Tkatchenko A. Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules. Sci Data 2024; 11:742. [PMID: 38972891 PMCID: PMC11228031 DOI: 10.1038/s41597-024-03521-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/13/2024] [Indexed: 07/09/2024] Open
Abstract
We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.
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Affiliation(s)
- Leonardo Medrano Sandonas
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg.
- Institute for Materials Science and Max Bergmann Center of Biomaterials, TU Dresden, 01062, Dresden, Germany.
| | - Dries Van Rompaey
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium.
| | - Alessio Fallani
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Mathias Hilfiker
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg
| | - David Hahn
- Computational Chemistry, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Laura Perez-Benito
- Computational Chemistry, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Jonas Verhoeven
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Gary Tresadern
- Computational Chemistry, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Joerg Kurt Wegner
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
- Drug Discovery Data Sciences (D3S), Johnson & Johnson Innovative Medicine, 301 Binney Street, MA 02142, Cambridge, USA
| | - Hugo Ceulemans
- Drug Discovery Data Sciences (D3S), Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511, Luxembourg City, Luxembourg.
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13
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Chen M, Jiang X, Zhang L, Chen X, Wen Y, Gu Z, Li X, Zheng M. The emergence of machine learning force fields in drug design. Med Res Rev 2024; 44:1147-1182. [PMID: 38173298 DOI: 10.1002/med.22008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/29/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.
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Affiliation(s)
- Mingan Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
- Lingang Laboratory, Shanghai, China
| | - Xinyu Jiang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Lehan Zhang
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoxu Chen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Yiming Wen
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Zhiyong Gu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
| | - Mingyue Zheng
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, China
- School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
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14
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Atz K, Cotos L, Isert C, Håkansson M, Focht D, Hilleke M, Nippa DF, Iff M, Ledergerber J, Schiebroek CCG, Romeo V, Hiss JA, Merk D, Schneider P, Kuhn B, Grether U, Schneider G. Prospective de novo drug design with deep interactome learning. Nat Commun 2024; 15:3408. [PMID: 38649351 PMCID: PMC11035696 DOI: 10.1038/s41467-024-47613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Maria Håkansson
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Dorota Focht
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Mattis Hilleke
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Michael Iff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Jann Ledergerber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Carl C G Schiebroek
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Valentina Romeo
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Jan A Hiss
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Daniel Merk
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Petra Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Bernd Kuhn
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
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15
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Williams DC, Inala N. Physics-Informed Generative Model for Drug-like Molecule Conformers. J Chem Inf Model 2024; 64:2988-3007. [PMID: 38486425 DOI: 10.1021/acs.jcim.3c01816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
We present a diffusion-based generative model for conformer generation. Our model is focused on the reproduction of the bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank and the Cambridge Structural Database.
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Affiliation(s)
- David C Williams
- Nobias Therapeutics, Inc., 144 S Whisman Rd, Suite C, Mountain View, California 94041, United States
| | - Neil Inala
- Nobias Therapeutics, Inc., 144 S Whisman Rd, Suite C, Mountain View, California 94041, United States
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16
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Zhang H, Liu S, You J, Liu C, Zheng S, Lu Z, Wang T, Zheng N, Shao B. Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning. NATURE COMPUTATIONAL SCIENCE 2024; 4:210-223. [PMID: 38467870 DOI: 10.1038/s43588-024-00605-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 02/07/2024] [Indexed: 03/13/2024]
Abstract
Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research. However, its accuracy is limited by the kinetic energy density functional, which is notoriously hard to approximate for non-periodic molecular systems. Here we propose M-OFDFT, an OFDFT approach capable of solving molecular systems using a deep learning functional model. We build the essential non-locality into the model, which is made affordable by the concise density representation as expansion coefficients under an atomic basis. With techniques to address unconventional learning challenges therein, M-OFDFT achieves a comparable accuracy to Kohn-Sham DFT on a wide range of molecules untouched by OFDFT before. More attractively, M-OFDFT extrapolates well to molecules much larger than those seen in training, which unleashes the appealing scaling of OFDFT for studying large molecules including proteins, representing an advancement of the accuracy-efficiency trade-off frontier in quantum chemistry.
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Affiliation(s)
- He Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
- Microsoft Research AI4Science, Beijing, China
| | - Siyuan Liu
- Microsoft Research AI4Science, Beijing, China
| | | | - Chang Liu
- Microsoft Research AI4Science, Beijing, China.
| | | | - Ziheng Lu
- Microsoft Research AI4Science, Beijing, China
| | - Tong Wang
- Microsoft Research AI4Science, Beijing, China
| | - Nanning Zheng
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China
| | - Bin Shao
- Microsoft Research AI4Science, Beijing, China.
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17
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Buterez D, Janet JP, Kiddle SJ, Oglic D, Lió P. Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting. Nat Commun 2024; 15:1517. [PMID: 38409255 PMCID: PMC11258334 DOI: 10.1038/s41467-024-45566-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 01/25/2024] [Indexed: 02/28/2024] Open
Abstract
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property of interest. This problem arises in discovery processes that rely on screening funnels for trading off the overall costs against throughput and accuracy. Typically, individual stages in these processes are loosely connected and each one generates data at different scale and fidelity. We consider this setup holistically and demonstrate empirically that existing transfer learning techniques for graph neural networks are generally unable to harness the information from multi-fidelity cascades. Here, we propose several effective transfer learning strategies and study them in transductive and inductive settings. Our analysis involves a collection of more than 28 million unique experimental protein-ligand interactions across 37 targets from drug discovery by high-throughput screening and 12 quantum properties from the dataset QMugs. The results indicate that transfer learning can improve the performance on sparse tasks by up to eight times while using an order of magnitude less high-fidelity training data. Moreover, the proposed methods consistently outperform existing transfer learning strategies for graph-structured data on drug discovery and quantum mechanics datasets.
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Affiliation(s)
- David Buterez
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.
| | - Jon Paul Janet
- Molecular AI, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Steven J Kiddle
- Data Science & Advanced Analytics, Data Science & AI, R&D, AstraZeneca, Cambridge, UK
| | - Dino Oglic
- Centre for AI, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Pietro Lió
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
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18
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Nippa DF, Atz K, Hohler R, Müller AT, Marx A, Bartelmus C, Wuitschik G, Marzuoli I, Jost V, Wolfard J, Binder M, Stepan AF, Konrad DB, Grether U, Martin RE, Schneider G. Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning. Nat Chem 2024; 16:239-248. [PMID: 37996732 PMCID: PMC10849962 DOI: 10.1038/s41557-023-01360-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 10/03/2023] [Indexed: 11/25/2023]
Abstract
Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4-5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F-score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization.
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Affiliation(s)
- David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Kenneth Atz
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Remo Hohler
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Alex T Müller
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Andreas Marx
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Christian Bartelmus
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Georg Wuitschik
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Irene Marzuoli
- Process Chemistry and Catalysis (PCC), F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Vera Jost
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Jens Wolfard
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Martin Binder
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Antonia F Stepan
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - David B Konrad
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Munich, Germany.
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
| | - Rainer E Martin
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland.
- ETH Singapore SEC Ltd, Singapore, Singapore.
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19
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Isert C, Atz K, Riniker S, Schneider G. Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning. RSC Adv 2024; 14:4492-4502. [PMID: 38312732 PMCID: PMC10835705 DOI: 10.1039/d3ra08650j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 01/19/2024] [Indexed: 02/06/2024] Open
Abstract
Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.
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Affiliation(s)
- Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland +41 44 633 73 27
| | - Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland +41 44 633 73 27
| | - Sereina Riniker
- ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland +41 44 633 73 27
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland +41 44 633 73 27
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20
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Gelžinytė E, Öeren M, Segall MD, Csányi G. Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. J Chem Theory Comput 2024; 20:164-177. [PMID: 38108269 PMCID: PMC10782450 DOI: 10.1021/acs.jctc.3c00710] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023]
Abstract
We present a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 data set, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific data sets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H BDE, which allows us to compare reaction energies of hydrogen abstraction, which is the rate-limiting step of the aliphatic hydroxylation reaction catalyzed by CYPs. On the "CYP 3A4" data set, MACE achieves a BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET model that directly predicts bond dissociation enthalpies. Finally, we highlight the smoothness of the MACE potential over paths of sp3C-H bond elongation and show that a minimal extension is enough for the MACE model to start finding reasonable minimum energy paths of methoxy radical-mediated hydrogen abstraction. Altogether, this work lays the ground for further extensions of scope in terms of chemical elements, (CYP-mediated) reaction classes and modeling the full reaction paths, not only BDEs.
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Affiliation(s)
- Elena Gelžinytė
- Engineering
Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.
| | - Mario Öeren
- Optibrium
Limited, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9GL, U.K.
| | - Matthew D. Segall
- Optibrium
Limited, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9GL, U.K.
| | - Gábor Csányi
- Engineering
Laboratory, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, U.K.
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21
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Folmsbee D, Koes DR, Hutchison GR. Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences. J Chem Inf Model 2023; 63:7401-7411. [PMID: 38000780 PMCID: PMC10716907 DOI: 10.1021/acs.jcim.3c01278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/07/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023]
Abstract
We performed exhaustive torsion sampling on more than 3 million compounds using the GFN2-xTB method and performed a comparison of experimental crystallographic and gas-phase conformers. Many conformer sampling methods derive torsional angle distributions from experimental crystallographic data, limiting the torsion preferences to molecules that must be stable, synthetically accessible, and able to be crystallized. In this work, we evaluate the differences in torsional preferences of experimental crystallographic geometries and gas-phase computed conformers from a broad selection of compounds to determine whether torsional angle distributions obtained from semiempirical methods are suitable priors for conformer sampling. We find that differences in torsion preferences can be mostly attributed to a lack of available experimental crystallographic data with small deviations derived from gas-phase geometry differences. GFN2 demonstrates the ability to provide accurate and reliable torsional preferences that can provide a basis for new methods free from the limitations of experimental data collection. We provide Gaussian-based fits and sampling distributions suitable for torsion sampling and propose an alternative to the widely used "experimental torsion and knowledge distance geometry" (ETKDG) method using quantum torsion-derived distance geometry (QTDG) methods.
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Affiliation(s)
- Dakota
L. Folmsbee
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
- Department
of Anesthesiology & Perioperative Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
| | - David R. Koes
- Department
of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States
| | - Geoffrey R. Hutchison
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
- Department
of Chemical & Petroleum Engineering, University of Pittsburgh, 3700 O’Hara Street, Pittsburgh, Pennsylvania 15261, United States
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22
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Stylianakis I, Zervos N, Lii JH, Pantazis DA, Kolocouris A. Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory. J Comput Aided Mol Des 2023; 37:607-656. [PMID: 37597063 PMCID: PMC10618395 DOI: 10.1007/s10822-023-00513-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/03/2023] [Indexed: 08/21/2023]
Abstract
We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger's force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree-Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol-1), followed by B3LYP (0.69 kcal mol-1) and the HF theories (0.81-1.0 kcal mol-1). As regards the force fields, the lowest errors were observed for the Allinger's force fields MM3-00 (1.28 kcal mol-1), ΜΜ3-96 (1.40 kcal mol-1) and the Halgren's MMFF94 force field (1.30 kcal mol-1) and then for the MM2-91 clones MMX (1.77 kcal mol-1) and MM+ (2.01 kcal mol-1) and MM4 (2.05 kcal mol-1). The DREIDING (3.63 kcal mol-1) and UFF (3.77 kcal mol-1) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.
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Affiliation(s)
- Ioannis Stylianakis
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece
| | - Nikolaos Zervos
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece
| | - Jenn-Huei Lii
- Department of Chemistry, National Changhua University of Education, Changhua City, Taiwan
| | - Dimitrios A Pantazis
- Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, 45470, Mülheim an der Ruhr, Germany
| | - Antonios Kolocouris
- Department of Medicinal Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimioupolis Zografou, 15771, Athens, Greece.
- Laboratory of Medicinal Chemistry, Section of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis-Zografou, 15771, Athens, Greece.
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23
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Park YJ, Kim H, Jo J, Yoon S. Deep contrastive learning of molecular conformation for efficient property prediction. NATURE COMPUTATIONAL SCIENCE 2023; 3:1015-1022. [PMID: 38177719 DOI: 10.1038/s43588-023-00560-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 10/31/2023] [Indexed: 01/06/2024]
Abstract
Data-driven deep learning algorithms provide accurate prediction of high-level quantum-chemical molecular properties. However, their inputs must be constrained to the same quantum-chemical level of geometric relaxation as the training dataset, limiting their flexibility. Adopting alternative cost-effective conformation generative methods introduces domain-shift problems, deteriorating prediction accuracy. Here we propose a deep contrastive learning-based domain-adaptation method called Local Atomic environment Contrastive Learning (LACL). LACL learns to alleviate the disparities in distribution between the two geometric conformations by comparing different conformation-generation methods. We found that LACL forms a domain-agnostic latent space that encapsulates the semantics of an atom's local atomic environment. LACL achieves quantum-chemical accuracy while circumventing the geometric relaxation bottleneck and could enable future application scenarios such as inverse molecular engineering and large-scale screening. Our approach is also generalizable from small organic molecules to long chains of biological and pharmacological molecules.
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Affiliation(s)
- Yang Jeong Park
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
- Institute of New Media and Communications, Seoul National University, Seoul, Republic of Korea.
- Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - HyunGi Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
| | - Jeonghee Jo
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea
- Institute of New Media and Communications, Seoul National University, Seoul, Republic of Korea
| | - Sungroh Yoon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
- Institute of New Media and Communications, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea.
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24
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Buterez D, Janet JP, Kiddle SJ, Oglic D, Liò P. Modelling local and general quantum mechanical properties with attention-based pooling. Commun Chem 2023; 6:262. [PMID: 38030692 PMCID: PMC10686994 DOI: 10.1038/s42004-023-01045-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Atom-centred neural networks represent the state-of-the-art for approximating the quantum chemical properties of molecules, such as internal energies. While the design of machine learning architectures that respect chemical principles has continued to advance, the final atom pooling operation that is necessary to convert from atomic to molecular representations in most models remains relatively undeveloped. The most common choices, sum and average pooling, compute molecular representations that are naturally a good fit for many physical properties, while satisfying properties such as permutation invariance which are desirable from a geometric deep learning perspective. However, there are growing concerns that such simplistic functions might have limited representational power, while also being suboptimal for physical properties that are highly localised or intensive. Based on recent advances in graph representation learning, we investigate the use of a learnable pooling function that leverages an attention mechanism to model interactions between atom representations. The proposed pooling operation is a drop-in replacement requiring no changes to any of the other architectural components. Using SchNet and DimeNet++ as starting models, we demonstrate consistent uplifts in performance compared to sum and mean pooling and a recent physics-aware pooling operation designed specifically for orbital energies, on several datasets, properties, and levels of theory, with up to 85% improvements depending on the specific task.
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Affiliation(s)
- David Buterez
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK.
| | - Jon Paul Janet
- Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, 431 50, Sweden
| | - Steven J Kiddle
- Data Science & Advanced Analytics, Data Science & AI, R&D, AstraZeneca, Cambridge, CB2 8PA, UK
| | - Dino Oglic
- Center for AI, Data Science & AI, R&D, AstraZeneca, Cambridge, CB2 8PA, UK
| | - Pietro Liò
- Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, UK
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25
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Nguyen NQ, Park S, Gim M, Kang J. MulinforCPI: enhancing precision of compound-protein interaction prediction through novel perspectives on multi-level information integration. Brief Bioinform 2023; 25:bbad484. [PMID: 38180829 PMCID: PMC10768804 DOI: 10.1093/bib/bbad484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/15/2023] [Accepted: 12/05/2023] [Indexed: 01/07/2024] Open
Abstract
Forecasting the interaction between compounds and proteins is crucial for discovering new drugs. However, previous sequence-based studies have not utilized three-dimensional (3D) information on compounds and proteins, such as atom coordinates and distance matrices, to predict binding affinity. Furthermore, numerous widely adopted computational techniques have relied on sequences of amino acid characters for protein representations. This approach may constrain the model's ability to capture meaningful biochemical features, impeding a more comprehensive understanding of the underlying proteins. Here, we propose a two-step deep learning strategy named MulinforCPI that incorporates transfer learning techniques with multi-level resolution features to overcome these limitations. Our approach leverages 3D information from both proteins and compounds and acquires a profound understanding of the atomic-level features of proteins. Besides, our research highlights the divide between first-principle and data-driven methods, offering new research prospects for compound-protein interaction tasks. We applied the proposed method to six datasets: Davis, Metz, KIBA, CASF-2016, DUD-E and BindingDB, to evaluate the effectiveness of our approach.
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Affiliation(s)
- Ngoc-Quang Nguyen
- Department of Computer Science and Engineering, Korea University, 02841, Seoul, Korea
| | - Sejeong Park
- Department of Computer Science and Engineering, Korea University, 02841, Seoul, Korea
- AIGEN Sciences, 04778, Seoul, Korea
| | - Mogan Gim
- Department of Computer Science and Engineering, Korea University, 02841, Seoul, Korea
| | - Jaewoo Kang
- Department of Computer Science and Engineering, Korea University, 02841, Seoul, Korea
- Interdisciplinary Graduate Program in Bioinformatics, Korea University, 02841, Seoul, Korea
- AIGEN Sciences, 04778, Seoul, Korea
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26
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Nippa DF, Atz K, Müller AT, Wolfard J, Isert C, Binder M, Scheidegger O, Konrad DB, Grether U, Martin RE, Schneider G. Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening. Commun Chem 2023; 6:256. [PMID: 37985850 PMCID: PMC10661846 DOI: 10.1038/s42004-023-01047-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/30/2023] [Indexed: 11/22/2023] Open
Abstract
Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.
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Affiliation(s)
- David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Kenneth Atz
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Alex T Müller
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Jens Wolfard
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Clemens Isert
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Martin Binder
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - Oliver Scheidegger
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland
| | - David B Konrad
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany.
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Rainer E Martin
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070, Basel, Switzerland.
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
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27
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Nandi S, Vegge T, Bhowmik A. MultiXC-QM9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods. Sci Data 2023; 10:783. [PMID: 37938558 PMCID: PMC10632468 DOI: 10.1038/s41597-023-02690-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/25/2023] [Indexed: 11/09/2023] Open
Abstract
Well curated extensive datasets have helped spur intense molecular machine learning (ML) method development activities over the last few years, encouraging nonchemists to be part of the effort as well. QM9 dataset is one of the benchmark databases for small molecules with molecular energies based on B3LYP functional. G4MP2 based energies of these molecules were published later. To enable a wide variety of ML tasks like transfer learning, delta learning, multitask learning, etc. with QM9 molecules, in this article, we introduce a new dataset with QM9 molecule energies estimated with 76 different DFT functionals and three different basis sets (228 energy numbers for each molecule). We additionally enumerated all possible A ↔ B monomolecular interconversions within the QM9 dataset and provided the reaction energies based on these 76 functionals, and basis sets. Lastly, we also provide the bond changes for all the 162 million reactions with the dataset to enable structure- and bond-based reaction energy prediction tools based on ML.
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Affiliation(s)
- Surajit Nandi
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej 301, 2800 Kongens Lyngby, Copenhagen, Denmark
| | - Tejs Vegge
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej 301, 2800 Kongens Lyngby, Copenhagen, Denmark
| | - Arghya Bhowmik
- Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej 301, 2800 Kongens Lyngby, Copenhagen, Denmark.
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28
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Lehner MT, Katzberger P, Maeder N, Schiebroek CC, Teetz J, Landrum GA, Riniker S. DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment. J Chem Inf Model 2023; 63:6014-6028. [PMID: 37738206 PMCID: PMC10565818 DOI: 10.1021/acs.jcim.3c00800] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 09/24/2023]
Abstract
We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines, as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source/open data from public repositories.
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Affiliation(s)
| | | | - Niels Maeder
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Carl C.G. Schiebroek
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jakob Teetz
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Gregory A. Landrum
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Sereina Riniker
- Department of Chemistry and
Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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29
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Nakata M, Maeda T. PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations. J Chem Inf Model 2023; 63:5734-5754. [PMID: 37677147 DOI: 10.1021/acs.jcim.3c00899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
The presented "PubChemQC B3LYP/6-31G*//PM6" data set is composed of the electronic properties of 85,938,443 molecules, encompassing a broad spectrum of molecules from essential compounds to biomolecules with a molecular weight up to 1000. These molecules account for 94.0% of the original PubChem Compound catalog as of August 29, 2016. The electronic properties, including orbitals, orbital energies, total energies, dipole moments, and other pertinent properties, were computed by using the B3LYP/6-31G* and PM6 methods. The data set, available in three formats, namely, GAMESS quantum chemistry program files, selected JSON output files, and a PostgreSQL database, provides researchers with the ability to query molecular properties. It is further subdivided into five subdata sets for more specific data. The first two subsets encompass molecules with carbon, hydrogen, oxygen, and nitrogen with molecular weights under 300 and 500, respectively. The third and fourth subsets incorporate molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, and chlorine, with molecular weights under 300 and 500, respectively. The fifth subset comprises molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, chlorine, sodium, potassium, magnesium, and calcium, with a molecular weight of under 500. The coefficients of determination for the highest occupied molecular orbital-lowest unoccupied molecular orbital energy gap range from 0.892 (for CHON500) to 0.803 (for the whole data set). These comprehensive results pave the way for applications in drug discovery and materials science, among others. The data sets can be accessed under the Creative Commons Attribution 4.0 International license at the following web address: https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.
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Affiliation(s)
- Maho Nakata
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Toshiyuki Maeda
- Software Technology and Artificial Intelligence Research Laboratory, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, Chiba 275-0016, Japan
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30
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Darby JP, Kovács DP, Batatia I, Caro MA, Hart GLW, Ortner C, Csányi G. Tensor-Reduced Atomic Density Representations. PHYSICAL REVIEW LETTERS 2023; 131:028001. [PMID: 37505943 DOI: 10.1103/physrevlett.131.028001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 04/18/2023] [Indexed: 07/30/2023]
Abstract
Density-based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modeling, and the visualization and analysis of material datasets. The standard mechanism used to incorporate chemical element information is to create separate densities for each element and form tensor products between them. This leads to a steep scaling in the size of the representation as the number of elements increases. Graph neural networks, which do not explicitly use density representations, escape this scaling by mapping the chemical element information into a fixed dimensional space in a learnable way. By exploiting symmetry, we recast this approach as tensor factorization of the standard neighbour-density-based descriptors and, using a new notation, identify connections to existing compression algorithms. In doing so, we form compact tensor-reduced representation of the local atomic environment whose size does not depend on the number of chemical elements, is systematically convergable, and therefore remains applicable to a wide range of data analysis and regression tasks.
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Affiliation(s)
- James P Darby
- Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom
| | - Dávid P Kovács
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom
| | - Ilyes Batatia
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom
- ENS Paris-Saclay, Université Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Miguel A Caro
- Department of Electrical Engineering and Automation, Aalto University, FIN-02150 Espoo, Finland
| | - Gus L W Hart
- Department of Physics and Astronomy, Brigham Young University, Provo, Utah, 84602, USA
| | - Christoph Ortner
- Department of Mathematics, University of British Columbia, 1984 Mathematics Road, Vancouver, British Columbia, Canada V6T 1Z2
| | - Gábor Csányi
- Engineering Laboratory, University of Cambridge, Cambridge, CB2 1PZ, United Kingdom
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31
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Buterez D, Janet JP, Kiddle SJ, Liò P. MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning. J Chem Inf Model 2023; 63:2667-2678. [PMID: 37058588 PMCID: PMC10170507 DOI: 10.1021/acs.jcim.2c01569] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Indexed: 04/16/2023]
Abstract
High-throughput screening (HTS), as one of the key techniques in drug discovery, is frequently used to identify promising drug candidates in a largely automated and cost-effective way. One of the necessary conditions for successful HTS campaigns is a large and diverse compound library, enabling hundreds of thousands of activity measurements per project. Such collections of data hold great promise for computational and experimental drug discovery efforts, especially when leveraged in combination with modern deep learning techniques, and can potentially lead to improved drug activity predictions and cheaper and more effective experimental design. However, existing collections of machine-learning-ready public datasets do not exploit the multiple data modalities present in real-world HTS projects. Thus, the largest fraction of experimental measurements, corresponding to hundreds of thousands of "noisy" activity values from primary screening, are effectively ignored in the majority of machine learning models of HTS data. To address these limitations, we introduce Multifidelity PubChem BioAssay (MF-PCBA), a curated collection of 60 datasets that includes two data modalities for each dataset, corresponding to primary and confirmatory screening, an aspect that we call multifidelity. Multifidelity data accurately reflect real-world HTS conventions and present a new, challenging task for machine learning: the integration of low- and high-fidelity measurements through molecular representation learning, taking into account the orders-of-magnitude difference in size between the primary and confirmatory screens. Here we detail the steps taken to assemble MF-PCBA in terms of data acquisition from PubChem and the filtering steps required to curate the raw data. We also provide an evaluation of a recent deep-learning-based method for multifidelity integration across the introduced datasets, demonstrating the benefit of leveraging all HTS modalities, and a discussion in terms of the roughness of the molecular activity landscape. In total, MF-PCBA contains over 16.6 million unique molecule-protein interactions. The datasets can be easily assembled by using the source code available at https://github.com/davidbuterez/mf-pcba.
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Affiliation(s)
- David Buterez
- Department
of Computer Science and Technology, University
of Cambridge, Cambridge CB3 0FD, U.K.
| | - Jon Paul Janet
- Molecular
AI, Discovery Sciences, R&D, AstraZeneca, 431 50 Gothenburg, Sweden
| | - Steven J. Kiddle
- Data
Science & Advanced Analytics, Data Science & Artificial Intelligence,
R&D, AstraZeneca, Cambridge CB2 8PA, U.K.
| | - Pietro Liò
- Department
of Computer Science and Technology, University
of Cambridge, Cambridge CB3 0FD, U.K.
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32
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Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023; 28:3906. [PMID: 37175316 PMCID: PMC10180087 DOI: 10.3390/molecules28093906] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023] Open
Abstract
The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann—La Roche Ltd., 4070 Basel, Switzerland;
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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33
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Isert C, Atz K, Schneider G. Structure-based drug design with geometric deep learning. Curr Opin Struct Biol 2023; 79:102548. [PMID: 36842415 DOI: 10.1016/j.sbi.2023.102548] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 01/16/2023] [Accepted: 01/24/2023] [Indexed: 02/26/2023]
Abstract
Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has been applied to macromolecular structures. This review provides an overview of the recent applications of geometric deep learning in bioorganic and medicinal chemistry, highlighting its potential for structure-based drug discovery and design. Emphasis is placed on molecular property prediction, ligand binding site and pose prediction, and structure-based de novo molecular design. The current challenges and opportunities are highlighted, and a forecast of the future of geometric deep learning for drug discovery is presented.
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Affiliation(s)
- Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, Zurich, 8093, Switzerland
| | - Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, Zurich, 8093, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, Zurich, 8093, Switzerland; ETH Singapore SEC Ltd, 1 CREATE Way, #06-01 CREATE Tower, Singapore, 8093, Singapore.
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Zeng J, Tao Y, Giese TJ, York DM. QDπ: A Quantum Deep Potential Interaction Model for Drug Discovery. J Chem Theory Comput 2023; 19:1261-1275. [PMID: 36696673 PMCID: PMC9992268 DOI: 10.1021/acs.jctc.2c01172] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
We report QDπ-v1.0 for modeling the internal energy of drug molecules containing H, C, N, and O atoms. The QDπ model is in the form of a quantum mechanical/machine learning potential correction (QM/Δ-MLP) that uses a fast third-order self-consistent density-functional tight-binding (DFTB3/3OB) model that is corrected to a quantitatively high-level of accuracy through a deep-learning potential (DeepPot-SE). The model has the advantage that it is able to properly treat electrostatic interactions and handle changes in charge/protonation states. The model is trained against reference data computed at the ωB97X/6-31G* level (as in the ANI-1x data set) and compared to several other approximate semiempirical and machine learning potentials (ANI-1x, ANI-2x, DFTB3, MNDO/d, AM1, PM6, GFN1-xTB, and GFN2-xTB). The QDπ model is demonstrated to be accurate for a wide range of intra- and intermolecular interactions (despite its intended use as an internal energy model) and has shown to perform exceptionally well for relative protonation/deprotonation energies and tautomers. An example application to model reactions involved in RNA strand cleavage catalyzed by protein and nucleic acid enzymes illustrates QDπ has average errors less than 0.5 kcal/mol, whereas the other models compared have errors over an order of magnitude greater. Taken together, this makes QDπ highly attractive as a potential force field model for drug discovery.
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Affiliation(s)
- Jinzhe Zeng
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Yujun Tao
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, NJ 08854, USA
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35
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Kříž K, Schmidt L, Andersson AT, Walz MM, van der Spoel D. An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation. J Chem Inf Model 2023; 63:412-431. [PMID: 36630710 PMCID: PMC9875315 DOI: 10.1021/acs.jcim.2c01127] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Indexed: 01/12/2023]
Abstract
Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.
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Affiliation(s)
- Kristian Kříž
- Department
of Cell and Molecular Biology, Uppsala University, Box 596, SE-75124Uppsala, Sweden
| | - Lisa Schmidt
- Faculty
of Biosciences, University of Heidelberg, Heidelberg69117, Germany
| | - Alfred T. Andersson
- Department
of Cell and Molecular Biology, Uppsala University, Box 596, SE-75124Uppsala, Sweden
| | - Marie-Madeleine Walz
- Department
of Cell and Molecular Biology, Uppsala University, Box 596, SE-75124Uppsala, Sweden
| | - David van der Spoel
- Department
of Cell and Molecular Biology, Uppsala University, Box 596, SE-75124Uppsala, Sweden
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36
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Isert C, Kromann JC, Stiefl N, Schneider G, Lewis RA. Machine Learning for Fast, Quantum Mechanics-Based Approximation of Drug Lipophilicity. ACS OMEGA 2023; 8:2046-2056. [PMID: 36687099 PMCID: PMC9850743 DOI: 10.1021/acsomega.2c05607] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
Lipophilicity, as measured by the partition coefficient between octanol and water (log P), is a key parameter in early drug discovery research. However, measuring log P experimentally is difficult for specific compounds and log P ranges. The resulting lack of reliable experimental data impedes development of accurate in silico models for such compounds. In certain discovery projects at Novartis focused on such compounds, a quantum mechanics (QM)-based tool for log P estimation has emerged as a valuable supplement to experimental measurements and as a preferred alternative to existing empirical models. However, this QM-based approach incurs a substantial computational cost, limiting its applicability to small series and prohibiting quick, interactive ideation. This work explores a set of machine learning models (Random Forest, Lasso, XGBoost, Chemprop, and Chemprop3D) to learn calculated log P values on both a public data set and an in-house data set to obtain a computationally affordable, QM-based estimation of drug lipophilicity. The message-passing neural network model Chemprop emerged as the best performing model with mean absolute errors of 0.44 and 0.34 log units for scaffold split test sets of the public and in-house data sets, respectively. Analysis of learning curves suggests that a further decrease in the test set error can be achieved by increasing the training set size. While models directly trained on experimental data perform better at approximating experimentally determined log P values than models trained on calculated values, we discuss the potential advantages of using calculated log P values going beyond the limits of experimental quantitation. We analyze the impact of the data set splitting strategy and gain insights into model failure modes. Potential use cases for the presented models include pre-screening of large compound collections and prioritization of compounds for full QM calculations.
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Affiliation(s)
- Clemens Isert
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, Vladimir-Prelog-Weg 4, 8093Zurich, Switzerland
- Novartis
Institutes for BioMedical Research, 4056Basel, Switzerland
| | - Jimmy C. Kromann
- Novartis
Institutes for BioMedical Research, 4056Basel, Switzerland
| | - Nikolaus Stiefl
- Novartis
Institutes for BioMedical Research, 4056Basel, Switzerland
| | - Gisbert Schneider
- Department
of Chemistry and Applied Biosciences, ETH
Zurich, Vladimir-Prelog-Weg 4, 8093Zurich, Switzerland
- ETH
Singapore SEC Ltd., 1
CREATE Way, #06-01 CREATE Tower138602, Singapore, Singapore
| | - Richard A. Lewis
- Novartis
Institutes for BioMedical Research, 4056Basel, Switzerland
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Eastman P, Behara PK, Dotson DL, Galvelis R, Herr JE, Horton JT, Mao Y, Chodera JD, Pritchard BP, Wang Y, De Fabritiis G, Markland TE. SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials. Sci Data 2023; 10:11. [PMID: 36599873 PMCID: PMC9813265 DOI: 10.1038/s41597-022-01882-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 12/01/2022] [Indexed: 01/05/2023] Open
Abstract
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe the SPICE dataset, a new quantum chemistry dataset for training potentials relevant to simulating drug-like small molecules interacting with proteins. It contains over 1.1 million conformations for a diverse set of small molecules, dimers, dipeptides, and solvated amino acids. It includes 15 elements, charged and uncharged molecules, and a wide range of covalent and non-covalent interactions. It provides both forces and energies calculated at the ωB97M-D3(BJ)/def2-TZVPPD level of theory, along with other useful quantities such as multipole moments and bond orders. We train a set of machine learning potentials on it and demonstrate that they can achieve chemical accuracy across a broad region of chemical space. It can serve as a valuable resource for the creation of transferable, ready to use potential functions for use in molecular simulations.
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Affiliation(s)
- Peter Eastman
- Department of Chemistry, Stanford University, Stanford, CA, 94305, USA.
| | - Pavan Kumar Behara
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, 92697, USA
| | - David L Dotson
- The Open Force Field Initiative, Open Molecular Software Foundation, Davis, CA, 95616, USA
| | | | - John E Herr
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Josh T Horton
- School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom
| | - Yuezhi Mao
- Department of Chemistry, Stanford University, Stanford, CA, 94305, USA
| | - John D Chodera
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Benjamin P Pritchard
- Molecular Sciences Software Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Yuanqing Wang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Graduate Program in Physiology, Biophysics, and Systems Biology, Weill Cornell Graduate School of Medical Sciences, New York, NY, 10065, USA
| | - Gianni De Fabritiis
- Acellera Labs, Doctor Trueta 183, 08005, Barcelona, Spain
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), Carrer Dr. Aiguader 88, 08003, Barcelona, Spain and ICREA, Passeig Lluis Companys 23, 08010, Barcelona, Spain
| | - Thomas E Markland
- Department of Chemistry, Stanford University, Stanford, CA, 94305, USA
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Atz K, Guba W, Grether U, Schneider G. Machine Learning and Computational Chemistry for the Endocannabinoid System. Methods Mol Biol 2023; 2576:477-493. [PMID: 36152211 DOI: 10.1007/978-1-0716-2728-0_39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
| | - Wolfgang Guba
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland
- ETH Singapore SEC Ltd, Singapore, Singapore
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39
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Combining machine‐learning and molecular‐modeling methods for drug‐target affinity predictions. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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