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Quantum Theory

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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...

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...

From genome to clinic: The power of translational bioinformatics in improving human health.

Advances in protein chemistry and structural biology
Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical...

Noncompact uniform universal approximation.

Neural networks : the official journal of the International Neural Network Society
The universal approximation theorem is generalised to uniform convergence on the (noncompact) input space R. All continuous functions that vanish at infinity can be uniformly approximated by neural networks with one hidden layer, for all activation f...

Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning.

The journal of physical chemistry. A
Solvatochromism occurs in both homogeneous solvents and more complex biological environments, such as proteins. While in both cases the solvatochromic effects report on the surroundings of the chromophore, their interpretation in proteins becomes mor...

From GPUs to AI and quantum: three waves of acceleration in bioinformatics.

Drug discovery today
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel acceler...

How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms.

Current opinion in structural biology
Molecular simulations are an essential asset in the first steps of drug design campaigns. However, the requirement of high-throughput limits applications mainly to qualitative approaches with low computational cost, but also low accuracy. Unlocking t...

Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways.

BMC bioinformatics
BACKGROUND: Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data.

Modeling Zinc Complexes Using Neural Networks.

Journal of chemical information and modeling
Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build...

Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning.

Xenobiotica; the fate of foreign compounds in biological systems
Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to predict and study the dominant routes of metabolism in the early stages of research.We describe the d...