4.8 Article

Freedom of design in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties

Related references

Note: Only part of the references are listed.
Article Chemistry, Multidisciplinary

Generative Models as an Emerging Paradigm in the Chemical Sciences

Olexandr Isayev et al.

Summary: Traditional computational approaches to chemical species design are limited by the need to compute properties for a large number of candidates. Inverse design methods use generative modeling to optimize chemical structures based on desired properties. This article provides an overview and critical analysis of popular generative algorithms and discusses their potential applications in chemistry.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2023)

Article Physics, Multidisciplinary

Four-Dimensional Scaling of Dipole Polarizability in Quantum Systems

Peter Szabo et al.

Summary: Polarizability is an important response property of physical and chemical systems, affecting intermolecular interactions, spectroscopic observables, and vacuum polarization. This study presents a universal four-dimensional scaling law to describe polarizability, applicable to both single-particle and many-particle quantum systems.

PHYSICAL REVIEW LETTERS (2022)

Article Multidisciplinary Sciences

QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules

Johannes Hoja et al.

Summary: The QM7-X dataset contains approximately 4.2 million equilibrium and non-equilibrium structures of small organic molecules, with a comprehensive coverage of various physicochemical properties. It is expected to play a critical role in the development of next-generation machine-learning models for exploring broader regions of chemical compound space and designing molecules with targeted properties.

SCIENTIFIC DATA (2021)

Article Multidisciplinary Sciences

Accelerated discovery of 3D printing materials using data-driven multiobjective optimization

Timothy Erps et al.

Summary: Additive manufacturing is a forefront technology in fabrication, but current materials often suffer from performance trade-offs. Researchers are proposing a machine learning approach to accelerate the discovery of materials with optimal trade-offs in mechanical performance, reducing the number of experiments and time needed for discovery.

SCIENCE ADVANCES (2021)

Article Chemistry, Physical

Structural exploration and properties of (BN)6 cluster via ab initio in combination with particle swarm optimization method

Ying-Qin Zhao et al.

Summary: The particle swarm optimization method and Density Functional Theory were used to search for isomers of the (BN)(6) cluster, leading to the discovery of new low energy structures and confirmation of stable configurations. The covalent interaction between B and N was demonstrated through topological analysis, supporting the calculated results.

THEORETICAL CHEMISTRY ACCOUNTS (2021)

Article Multidisciplinary Sciences

Bias free multiobjective active learning for materials design and discovery

Kevin Maik Jablonka et al.

Summary: This study utilizes an active learning algorithm and Pareto dominance relation to compute a set of Pareto optimal materials for multi-objective material design. By conducting molecular simulations, the number of materials that need to be evaluated is drastically reduced, enhancing design efficiency.

NATURE COMMUNICATIONS (2021)

Review Chemistry, Multidisciplinary

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

John A. Keith et al.

Summary: The article discusses the potential impact of machine learning models on chemical sciences and emphasizes the importance of collaboration between expertise in computer science and physical sciences. It provides concise tutorials of computational chemistry and machine learning methods, and demonstrates how they can be used together to provide insightful predictions.

CHEMICAL REVIEWS (2021)

Review Chemistry, Multidisciplinary

Ab Initio Machine Learning in Chemical Compound Space

Bing Huang et al.

Summary: Chemical compound space (CCS) is vast and exploring it using modern machine learning techniques based on quantum mechanics principles can improve computational efficiency while maintaining predictive power. These methods have potential applications in discovering novel molecules or materials with desirable properties.

CHEMICAL REVIEWS (2021)

Review Chemistry, Multidisciplinary

Machine Learning for Chemical Reactions

Markus Meuwly

Summary: Machine learning techniques have a long history in the field of chemical reactions, being able to address complex problems involving both computation and experiments. These techniques can develop models consistent with experimental knowledge, handle problems intractable to conventional approaches, and simulate reactive networks in combustion.

CHEMICAL REVIEWS (2021)

Review Chemistry, Multidisciplinary

Performance-Based Screening of Porous Materials for Carbon Capture

Amir H. Farmahini et al.

Summary: Computational screening methods have revolutionized the discovery and design of new materials and processes, with recent efforts focusing on multiscale and performance-based screening workflows. The objective is to review the current status, potential impact, and challenges of this new approach, providing a practical guide for scientific communities and proposing future directions for the field. Challenges include data availability, model consistency, and reproducibility, with a comprehensive compilation of tools and parameters required for multiscale screening.

CHEMICAL REVIEWS (2021)

Review Chemistry, Multidisciplinary

Gaussian Process Regression for Materials and Molecules

Volker L. Deringer et al.

Summary: This paper introduces the application of Gaussian process regression machine learning in computational materials science and chemistry, focusing on the regression of atomistic properties, including the construction of interatomic potentials, force fields, and fitting of various quantities. Methodological aspects of reference data generation, representation, regression, and model validation are reviewed, along with a survey of applications in chemistry and materials science and an outline of future development vision.

CHEMICAL REVIEWS (2021)

Article Chemistry, Medicinal

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus

Victor O. Gawriljuk et al.

Summary: Yellow fever is an acute viral hemorrhagic disease transmitted by infected mosquitoes, with a lack of specific small molecule drug treatment. Machine learning plays a significant role in drug discovery.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

Article Chemistry, Physical

Electron confinement meet electron delocalization: non-additivity and finite-size effects in the polarizabilities and dispersion coefficients of the fullerenes

Ka Un Lao et al.

Summary: This study utilized finite-field derivative techniques and density functional theory to compute static isotropic polarizability series for fullerenes, analyzing the unique electron structure effects on quantum mechanical responses. The results demonstrate the limits and enhancements in response to electric field perturbations due to the quasi-spherical cage-like structures and encapsulated void spaces of finite-sized fullerenes. Additionally, the study computed frequency-dependent polarizabilities and molecular dispersion coefficients to describe the non-trivial van der Waals interactions in fullerene-based systems.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2021)

Article Biochemistry & Molecular Biology

A Deep Learning Approach to Antibiotic Discovery

Jonathan M. Stokes et al.

Article Chemistry, Physical

Enabling Catalyst Discovery through Machine Learning and High-Throughput Experimentation

Travis Williams et al.

CHEMISTRY OF MATERIALS (2020)

Article Chemistry, Physical

Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization

Zachary del Rosario et al.

JOURNAL OF CHEMICAL PHYSICS (2020)

Review Chemistry, Multidisciplinary

Exploring chemical compound space with quantum-based machine learning

O. Anatole von Lilienfeld et al.

NATURE REVIEWS CHEMISTRY (2020)

Review Chemistry, Multidisciplinary

Polymer-Based Batteries-Flexible and Thin Energy Storage Systems

Martin D. Hager et al.

ADVANCED MATERIALS (2020)

Editorial Material Multidisciplinary Sciences

Machine learning for chemical discovery

Alexandre Tkatchenko

NATURE COMMUNICATIONS (2020)

Article Computer Science, Information Systems

Pymoo: Multi-Objective Optimization in Python

Julian Blank et al.

IEEE ACCESS (2020)

Review Chemistry, Multidisciplinary

QSAR without borders

Eugene N. Muratov et al.

CHEMICAL SOCIETY REVIEWS (2020)

Article Multidisciplinary Sciences

Accurate molecular polarizabilities with coupled cluster theory and machine learning

David M. Wilkins et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Review Biotechnology & Applied Microbiology

Applications of machine learning in drug discovery and development

Jessica Vamathevan et al.

NATURE REVIEWS DRUG DISCOVERY (2019)

Review Nanoscience & Nanotechnology

Designing polymers for advanced battery chemistries

Jeffrey Lopez et al.

NATURE REVIEWS MATERIALS (2019)

Article Multidisciplinary Sciences

Quantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases

Yang Yang et al.

SCIENTIFIC DATA (2019)

Article Chemistry, Physical

Understanding non-covalent interactions in larger molecular complexes from first principles

Yasmine S. Al-Hamdani et al.

JOURNAL OF CHEMICAL PHYSICS (2019)

Article Physics, Multidisciplinary

Influence of Pore Size on the van der Waals Interaction in Two-Dimensional Molecules and Materials

Yan Yang et al.

PHYSICAL REVIEW LETTERS (2019)

Review Pharmacology & Pharmacy

Challenges with multi-objective QSAR in drug discovery

George Lambrinidis et al.

EXPERT OPINION ON DRUG DISCOVERY (2018)

Article Chemistry, Multidisciplinary

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Rafael Gomez-Bombarelli et al.

ACS CENTRAL SCIENCE (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Article Chemistry, Physical

Efficient search of compositional space for hybrid organic-inorganic perovskites via Bayesian optimization

Henry C. Herbol et al.

NPJ COMPUTATIONAL MATERIALS (2018)

Article Physics, Multidisciplinary

Quantum-Mechanical Relation between Atomic Dipole Polarizability and the van der Waals Radius

Dmitry Fedorov et al.

PHYSICAL REVIEW LETTERS (2018)

Article Multidisciplinary Sciences

Discovering de novo peptide substrates for enzymes using machine learning

Lorillee Tallorin et al.

NATURE COMMUNICATIONS (2018)

Article Chemistry, Multidisciplinary

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

J. S. Smith et al.

CHEMICAL SCIENCE (2017)

Article Multidisciplinary Sciences

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

Justin S. Smith et al.

SCIENTIFIC DATA (2017)

Article Multidisciplinary Sciences

Machine learning unifies the modeling of materials and molecules

Albert P. Bartok et al.

SCIENCE ADVANCES (2017)

Article Chemistry, Physical

Communication: Charge-population based dispersion interactions for molecules and materials

Martin Stoehr et al.

JOURNAL OF CHEMICAL PHYSICS (2016)

Article Multidisciplinary Sciences

Accelerated search for materials with targeted properties by adaptive design

Dezhen Xue et al.

NATURE COMMUNICATIONS (2016)

Article Chemistry, Physical

Long-range correlation energy calculated from coupled atomic response functions

Alberto Ambrosetti et al.

JOURNAL OF CHEMICAL PHYSICS (2014)

Article Chemistry, Medicinal

QSAR Modeling: Where Have You Been? Where Are You Going To?

Artem Cherkasov et al.

JOURNAL OF MEDICINAL CHEMISTRY (2014)

Article Multidisciplinary Sciences

Quantum chemistry structures and properties of 134 kilo molecules

Raghunathan Ramakrishnan et al.

SCIENTIFIC DATA (2014)

Article Chemistry, Physical

Interatomic methods for the dispersion energy derived from the adiabatic connection fluctuation-dissipation theorem

Alexandre Tkatchenko et al.

JOURNAL OF CHEMICAL PHYSICS (2013)

Article Physics, Multidisciplinary

Machine learning of molecular electronic properties in chemical compound space

Gregoire Montavon et al.

NEW JOURNAL OF PHYSICS (2013)

Review Biochemistry & Molecular Biology

Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database

Jean-Louis Reymond et al.

ACS CHEMICAL NEUROSCIENCE (2012)

Review Chemistry, Multidisciplinary

Quantitative Structure-Property Relationship Modeling of Diverse Materials Properties

Tu Le et al.

CHEMICAL REVIEWS (2012)

Article Chemistry, Medicinal

Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

Lars Ruddigkeit et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2012)

Article Physics, Multidisciplinary

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Matthias Rupp et al.

PHYSICAL REVIEW LETTERS (2012)

Article Physics, Multidisciplinary

Accurate and Efficient Method for Many-Body van der Waals Interactions

Alexandre Tkatchenko et al.

PHYSICAL REVIEW LETTERS (2012)

Article Multidisciplinary Sciences

Collective many-body van der Waals interactions in molecular systems

Robert A. DiStasio et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2012)

Article Chemistry, Physical

DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB)

Michael Gaus et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2011)

Review Computer Science, Interdisciplinary Applications

Ab initio molecular simulations with numeric atom-centered orbitals

Volker Blum et al.

COMPUTER PHYSICS COMMUNICATIONS (2009)

Article Computer Science, Interdisciplinary Applications

Efficient O(N) integration for all-electron electronic structure calculation using numeric basis functions

V. Havu et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2009)

Article Chemistry, Multidisciplinary

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

Lorenz C. Blum et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2009)

Article Chemistry, Multidisciplinary

Virtual exploration of the small-molecule chemical universe below 160 daltons

T Fink et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2005)

Article Chemistry, Physical

Atomic polarizability, volume and ionization energy

P Politzer et al.

JOURNAL OF CHEMICAL PHYSICS (2002)

Article Computer Science, Artificial Intelligence

A fast and elitist multiobjective genetic algorithm: NSGA-II

K Deb et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2002)