Search alternatives:
process optimization » model optimization (Expand Search)
library based » laboratory based (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
swarm » warm (Expand Search)
process optimization » model optimization (Expand Search)
library based » laboratory based (Expand Search)
based process » based processes (Expand Search), based probes (Expand Search), based proteins (Expand Search)
swarm » warm (Expand Search)
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MSE for ILSTM algorithm in binary classification.
Published 2023“…The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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<i>De Novo</i> Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization
Published 2020“…In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated <i>de novo</i> design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. …”
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An optimal solution for the HFS instance.
Published 2025“…In recent years, due to the advantages of nonlinear access and fully parallel processing, the probe machine has shown powerful computing capabilities and promising applications in solving various combinatorial optimization problems. …”
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Comparison based on hard instances from [79].
Published 2025“…In recent years, due to the advantages of nonlinear access and fully parallel processing, the probe machine has shown powerful computing capabilities and promising applications in solving various combinatorial optimization problems. …”
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Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model
Published 2019“…Because of the semipredictive nature of the symmetric eNRTL-SAC model, the segment parameter regression is a critical step for solubility prediction accuracy. A particle swarm optimization algorithm is incorporated to preregress conceptual segment parameters of solutes. …”
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