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property optimization » process optimization (Expand Search), policy optimization (Expand Search), robust optimization (Expand Search)
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pair based » paper based (Expand Search), pcr based (Expand Search)
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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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2
RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms
Published 2025“…The Rosetta automated Monte Carlo reaction-based ligand design (RosettaAMRLD) integrates a Monte Carlo Metropolis (MCM) algorithm and reaction-driven molecule proposal to enhance structure-based <i>de novo</i> drug discovery. …”
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3
Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start
Published 2025“…In response to these challenges, this work presents a method to fine-tune a genetic algorithm for CAMD. The proposed method builds on the COSMO-CAMD framework that utilizes a genetic algorithm for solving optimization-based molecular design problems and COSMO-RS for predicting physical properties of molecules. …”
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Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start
Published 2025“…In response to these challenges, this work presents a method to fine-tune a genetic algorithm for CAMD. The proposed method builds on the COSMO-CAMD framework that utilizes a genetic algorithm for solving optimization-based molecular design problems and COSMO-RS for predicting physical properties of molecules. …”
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5
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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6
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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8
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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9
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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10
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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11
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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12
FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
Published 2024“…In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. …”
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<i>hi</i>PRS algorithm process flow.
Published 2023“…From this dataset we can compute the MI between each interaction and the outcome and <b>(D)</b> obtain a ranked list (<i>I</i><sub><i>δ</i></sub>) based on this metric. <b>(E)</b> Starting from the interaction at the top of <i>I</i><sub><i>δ</i></sub>, <i>hi</i>PRS constructs <i>I</i><sub><i>K</i></sub>, selecting <i>K</i> (where <i>K</i> is user-specified) terms through the greedy optimization of the ratio between MI (<i>relevance</i>) and a suitable measure of similarity for interactions (<i>redundancy)</i> (cf. …”
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15
Cheminformatics-Guided Cell-Free Exploration of Peptide Natural Products
Published 2024“…To assess the peptide NP space that is directly accessible to current cell-free technologies, we developed a peptide parsing algorithm that breaks down peptide NPs into building blocks based on ribosomal translation logic. …”
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Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf
Published 2020“…And we propose two new multilabel uses of the Common Spatial Pattern (CSP) algorithm to optimize the signal-to-noise ratio, namely MC2CMI and MC2SMI approaches. …”
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Supporting data for "clinical-oriented surgical planning based on finite element method and automate-generated surgical templates assisting the spinal surgery"
Published 2024“…Remaining algorithm needed was reimplemented from open-source libraries.…”
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hIPPYlib: An Extensible Software Framework for Large-scale Inverse Problems
Published 2019“…The key property of the algorithms implemented in hIPPYlib is that the solution is computed at a cost, measured in forward PDE solves, that is independent of the parameter dimension. …”
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Seed mix selection model
Published 2022“…The model thus requires three types of data presented as matrices in order to calculate the maximum number of bee species supported by a given seed mix: 1) adult phenology of each bee species, where each cell represents whether or not that bee species was observed in the data during a given time period, 2) flowering phenology of plants, where each cell represents whether or not a bee was collected from that plant species during a given time period, and 3) pairwise interactions between plant species and bee species, where each cell represents whether each plant-bee species pair was observed interacting in the data.</p> <p> </p> <p>We applied the seed mix selection model using a binary genetic algorithm to select seed mixes (R package ‘GA’; Scrucca 2013; Scrucca 2017). …”