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Computer Simulation

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Toward Dose Prediction at Point of Design.

Journal of medicinal chemistry
Human dose prediction (HDP) is a useful tool for compound optimization in preclinical drug discovery. We describe here our exclusively in silico HDP strategy to triage compound designs for synthesis and experimental profiling. Our goal is a model tha...

Output sampling synchronization and state estimation in flux-charge domain memristive neural networks with leakage and time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This paper theoretically explores the coexistence of synchronization and state estimation analysis through output sampling measures for a class of memristive neural networks operating within the flux-charge domain. These networks are subject to const...

A simple remedy for failure modes in physics informed neural networks.

Neural networks : the official journal of the International Neural Network Society
Physics-informed neural networks (PINNs) have shown promising results in solving a wide range of problems involving partial differential equations (PDEs). Nevertheless, there are several instances of the failure of PINNs when PDEs become more complex...

A Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes.

Journal of computational biology : a journal of computational molecular cell biology
The development of computational models for the prediction of cardiac cellular dynamics remains a challenge due to the lack of first-principled mathematical models. We develop a novel machine-learning approach hybridizing physics simulation and graph...

Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction.

The British journal of mathematical and statistical psychology
Ordinal responses commonly occur in psychology, e.g., through school grades or rating scales. Where traditionally parametric statistical models like the proportional odds model have been used, machine learning (ML) methods such as random forest (RF) ...

FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction.

Molecular pharmaceutics
The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubi...

Advancements in exponential synchronization and encryption techniques: Quaternion-Valued Artificial Neural Networks with two-sided coefficients.

Neural networks : the official journal of the International Neural Network Society
This paper presents cutting-edge advancements in exponential synchronization and encryption techniques, focusing on Quaternion-Valued Artificial Neural Networks (QVANNs) that incorporate two-sided coefficients. The study introduces a novel approach t...

Learning soft tissue deformation from incremental simulations.

Medical physics
BACKGROUND: Surgical planning for orthognathic procedures demands swift and accurate biomechanical modeling of facial soft tissues. Efficient simulations are vital in the clinical pipeline, as surgeons may iterate through multiple plans. Biomechanica...

Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of rheumatoid arthritis: Virtual screening to comprehensive in silico investigation.

PloS one
Rheumatoid arthritis (RA) affects an estimated 0.1% to 2.0% of the world's population, leading to a substantial impact on global health. The adverse effects and toxicity associated with conventional RA treatment pathways underscore the critical need ...

Classification of offshore wind grid-connected power quality disturbances based on fast S-transform and CPO-optimized convolutional neural network.

PloS one
The large-scale integration of offshore wind power into the power grid has brought serious challenges to the power system power quality. Aiming at the problem of power quality disturbance detection and classification, this paper proposes a novel algo...