AIMC Topic: Computer Simulation

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In silico-driven protocol for hit-to-lead optimization: a case study on PDE9A inhibitors.

Journal of computer-aided molecular design
Hit-to-lead (H2L) optimization is a critical stage in small-molecule drug discovery, where efficient exploration of chemical space is required to identify promising lead compounds. Conventional H2L workflows rely on iterative synthesis and experiment...

Unraveling the Mechanisms of Osteoporosis Triggered by Methylparaben and Monomethyl Phthalate through Integrated Mendelian Randomization, In Silico Simulations, and Experimental Validation.

Environmental science & technology
Endocrine-disrupting chemicals (EDCs) are pervasive environmental hazards that have been linked to osteoporosis (OP), though causal mechanisms remain elusive. Employing an integrated multiomics framework, this study combined bidirectional Mendelian r...

VirMolAnalyte: An AI-Driven Metabolite Annotation Tool.

Analytical chemistry
Metabolites play a crucial role in sustaining biological activities and are also a significant source of new drug development. Nuclear magnetic resonance (NMR) spectroscopy is one of the most important tools for identifying the structures of the meta...

Numerical computation of the stochastic hepatitis B model using feed forward neural network and real data.

Scientific reports
Hepatitis B is a global health burden and can persist for years, with nearly two billion infections worldwide, where its spread is influenced by environmental heterogeneity, host-pathogen interactions, and vaccination-induced immune variability. Prop...

Large-scale modeling of axonal dynamic responses via deep learning.

Biomechanics and modeling in mechanobiology
Large-scale axonal dynamic simulation is critical to study white matter injury but is prohibitive in computational cost. We solve this challenge by training a convolutional neural network (CNN) that takes fiber strain profiles as inputs to instantly ...

Virtual Brain Inference (VBI), a flexible and integrative toolkit for efficient probabilistic inference on whole-brain models.

eLife
Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling-also known as virtual brain modeling-combines computational ...

Learning structured population models from data with WSINDy.

PLoS computational biology
Characteristics of individuals in a population, such as age and size, play a key role in determining how populations change over time. In contexts of population dynamics, identifying effective model features, such as fecundity and mortality rates, is...

Capsule-based federated reinforcement learning adaptive sliding mode for anomaly detection and control of floating wind turbines.

PloS one
Floating wind turbines (FWTs) are now recognized as one of the most effective and affordable renewable energy sources. However, their performance is strongly influenced by dynamic environmental conditions, particularly sea waves under significant osc...

Advances in surrogate modeling for biological agent-based simulations: trends, challenges, and future prospects.

Journal of mathematical biology
Agent-based modeling (ABM) is a powerful computational approach for studying complex biological and biomedical systems, yet its widespread use remains limited by significant computational demands. As models become increasingly sophisticated, the numb...

ML-MAGES enables multivariate genetic association analyses with genes and effect size shrinkage.

Genome research
A fundamental goal of genetics is to identify which and how genetic variants are associated with a trait, often using the regression results from genome-wide association (GWA) studies. Important methodological challenges account for inflation in GWA ...