Current Issues in Molecular Biology

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Dear Colleagues,

With the rapid development in deep-learning algorithms, artificial neural networks, and a deeper understanding of mental disorders at the neuronal level, we have witnessed the giant advancement of computational neuroscience in recent years, including computational methodologies, biological traits, and even the artificial-intelligence-based theories of mind. Such advancement has been especially prominent within the field of memory and mental-related research, in which the combination of the personalized genome, optogenetics, and in silico simulation of neural networks within brain regions made it possible to interpret and model higher neural functionalities.

Here, we invite researchers from all spectra and disciplines of computational neuroscience to contribute their investigation outputs to this Special Issue, including, but not limited to, synaptic modeling and regulation, neural network patterning and topological analysis, EEG/fMRI-based imaging observations and functional brain mass modeling, information theory, and other multidisciplinary background studies.

We sincerely look forward to receiving your valuable contributions.

Dr. Siwei Zhang
Guest Editor

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Keywords

computational neuroscience
neural networks
in silico neuronal circuits
signal modulation
synapse plasticity
deep learning
mental simulation
mind theory
complex system
convolutional neural network (CNN)

Published Papers (1 paper)

Research

17 pages, 3898 KiB

Open AccessArticle

MIFNN: Molecular Information Feature Extraction and Fusion Deep Neural Network for Screening Potential Drugs

by Jingjing Wang, Hongzhen Li, Wenhan Zhao, Tinglin Pang, Zengzhao Sun, Bo Zhang and Huaqiang Xu

Curr. Issues Mol. Biol. 2022, 44(11), 5638-5654; https://doi.org/10.3390/cimb44110382 - 13 Nov 2022

Viewed by 2184

Abstract

Molecular property prediction is essential for drug screening and reducing the cost of drug discovery. Current approaches combined with deep learning for drug prediction have proven their viability. Based on the previous deep learning networks, we propose the Molecular Information Fusion Neural Network (MIFNN). The features of MIFNN are as follows: (1) we extracted directed molecular information using 1D-CNN and the Morgan fingerprint using 2D-CNN to obtain more comprehensive feature information; (2) we fused two molecular features from one-dimensional and two-dimensional space, and we used the directed message-passing method to reduce the repeated collection of information and improve efficiency; (3) we used a bidirectional long short-term memory and attention module to adjust the molecular feature information and improve classification accuracy; (4) we used the particle swarm optimization algorithm to improve the traditional support vector machine. We tested the performance of the model on eight publicly available datasets. In addition to comparing the overall classification capability with the baseline model, we conducted a series of ablation experiments to verify the optimization of different modules in the model. Compared with the baseline model, our model achieved a maximum improvement of 14% on the ToxCast dataset. The performance was very stable on most datasets. On the basis of the current experimental results, MIFNN performed better than previous models on the datasets applied in this paper. Full article

(This article belongs to the Special Issue Advances in Computational Neuroscience)

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