AIMC Topic: Quantum Theory

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Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.

Nature reviews. Drug discovery
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rap...

Quantum computing and machine learning for Arabic language sentiment classification in social media.

Scientific reports
With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown grea...

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment.

Journal of chemical information and modeling
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 traine...

Developing a User-Friendly Code for the Fast Estimation of Well-Behaved Real-Space Partial Charges.

Journal of chemical information and modeling
The Quantum Theory of Atoms in Molecules (QTAIM) provides an intuitive, yet physically sound, strategy to determine the partial charges of any chemical system relying on the topology induced by the electron density ρ() . In a previous work [ , , 0141...

Quantum computing for near-term applications in generative chemistry and drug discovery.

Drug discovery today
In recent years, drug discovery and life sciences have been revolutionized with machine learning and artificial intelligence (AI) methods. Quantum computing is touted to be the next most significant leap in technology; one of the main early practical...

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.

Journal of chemical information and modeling
De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learni...

An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.

Molecules (Basel, Switzerland)
Accurate conformational energetics of molecules are of great significance to understand maby chemical properties. They are also fundamental for high-quality parameterization of force fields. Traditionally, accurate conformational profiles are obtaine...

DeePKS + ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials.

The journal of physical chemistry. A
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as de...

Integration of Quantum Chemistry, Statistical Mechanics, and Artificial Intelligence for Computational Spectroscopy: The UV-Vis Spectrum of TEMPO Radical in Different Solvents.

Journal of chemical theory and computation
The ongoing integration of quantum chemistry, statistical mechanics, and artificial intelligence is paving the route toward more effective and accurate strategies for the investigation of the spectroscopic properties of medium-to-large size chromopho...

Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions.

Journal of chemical theory and computation
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous studies focuses on generating predictions for only a f...