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model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
wolf optimization » whale optimization (Expand Search), swarm optimization (Expand Search), _ optimization (Expand Search)
binary basic » binary mask (Expand Search)
sites based » scores based (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
wolf optimization » whale optimization (Expand Search), swarm optimization (Expand Search), _ optimization (Expand Search)
binary basic » binary mask (Expand Search)
sites based » scores based (Expand Search)
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181
Image 2_AI-driven innovation in antibody-drug conjugate design.jpeg
Published 2025“…This review is organized into six sections: (1) the progression from traditional modeling approaches to AI-driven design of individual ADC components; (2) the application of deep learning (DL) to antibody structure prediction and identification of optimal conjugation sites; (3) the use of AI/ML models for forecasting pharmacokinetic properties and toxicity profiles; (4) emerging generative algorithms for antibody sequence diversification and affinity optimization; (5) case studies demonstrating the integration of computational tools with experimental pipelines, including systems that link in silico predictions to high-throughput validation; and (6) persistent challenges, including data sparsity, model interpretability, validation complexity, and regulatory considerations. …”
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182
Image 1_AI-driven innovation in antibody-drug conjugate design.jpeg
Published 2025“…This review is organized into six sections: (1) the progression from traditional modeling approaches to AI-driven design of individual ADC components; (2) the application of deep learning (DL) to antibody structure prediction and identification of optimal conjugation sites; (3) the use of AI/ML models for forecasting pharmacokinetic properties and toxicity profiles; (4) emerging generative algorithms for antibody sequence diversification and affinity optimization; (5) case studies demonstrating the integration of computational tools with experimental pipelines, including systems that link in silico predictions to high-throughput validation; and (6) persistent challenges, including data sparsity, model interpretability, validation complexity, and regulatory considerations. …”
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183
Effects of Gas-Phase Conditions and Particle Size on the Properties of Cu(111)-Supported Zn<sub><i>y</i></sub>O<sub><i>x</i></sub> Particles Revealed by Global Optimization and Ab...
Published 2019“…In this study, we have applied an extensive and systematic approach combining global optimization based on an evolutionary algorithm with atomistic ab initio thermodynamics for finding stable structures of a relevant material for catalytic methanol synthesis: Cu(111)-supported Zn<sub><i>y</i></sub>O<sub><i>x</i></sub> clusters. …”
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184
Effects of Gas-Phase Conditions and Particle Size on the Properties of Cu(111)-Supported Zn<sub><i>y</i></sub>O<sub><i>x</i></sub> Particles Revealed by Global Optimization and Ab...
Published 2019“…In this study, we have applied an extensive and systematic approach combining global optimization based on an evolutionary algorithm with atomistic ab initio thermodynamics for finding stable structures of a relevant material for catalytic methanol synthesis: Cu(111)-supported Zn<sub><i>y</i></sub>O<sub><i>x</i></sub> clusters. …”
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185
Table_3_pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.XLSX
Published 2021“…Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. …”
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186
Table_4_pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.DOCX
Published 2021“…Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. …”
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187
Image_1_pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.TIF
Published 2021“…Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. …”
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188
Table_2_pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.XLSX
Published 2021“…Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. …”
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189
Image_3_pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.TIF
Published 2021“…Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. …”
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190
Table_1_pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.XLSX
Published 2021“…Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. …”
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191
Image_2_pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework.TIF
Published 2021“…Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. …”
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192
Acceleration of Inverse Molecular Design by Using Predictive Techniques
Published 2019“…This study addresses one of the most important drawbacks inherently related to molecular searches in chemical compound space by greedy algorithms such as Best First Search and Genetic Algorithm, i.e., the large computational cost required to optimize one or more quantum-chemical properties. …”
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193
Vehicle transportation plan.
Published 2025“…A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. …”
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194
Hypervolumes of each result.
Published 2025“…A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. …”
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195
Infectious medical wastes transport system.
Published 2025“…A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. …”
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196
Comparison of calculation results.
Published 2025“…A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. …”
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197
This is Vehicle transportation plan.
Published 2025“…A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. …”
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198
Node coordinates and production.
Published 2025“…A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. …”
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199
Schematic diagram of virus spread range.
Published 2025“…A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. …”
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200
Solution obtained by NSGA-II (Instance #2).
Published 2025“…A segmented collection approach is used to model the medical waste collection process. An optimization method for medical waste collection site selection and vehicle routing is proposed. …”