<i>De Novo</i> Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization by Alberga Domenico (9356272)

Subjects:

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design by Shu-Sen Chen (1777108)

Subjects:

RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms by Yidan Tang (6623693)

“…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. …”

Face recognition algorithm flow based on the PCA. by Na Lin (69470)

Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start by Yifan Wang (380120)

“…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. …”

Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start by Yifan Wang (380120)

“…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. …”

An optimal solution for the HFS instance. by Xiang Tian (4369285)

“…Secondly, based on the data libraries of the IPMMPO, two tuple sets suitable for constraint programming modeling are further designed as data preprocessing. …”

Acceleration of Inverse Molecular Design by Using Predictive Techniques by Jos L. Teunissen (1911856)

“…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. …”

Comparison based on hard instances from [79]. by Xiang Tian (4369285)

“…Secondly, based on the data libraries of the IPMMPO, two tuple sets suitable for constraint programming modeling are further designed as data preprocessing. …”

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

“…In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist’s toolbox to enhance the efficiency of both hit optimization and candidate design. …”

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

“…In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist’s toolbox to enhance the efficiency of both hit optimization and candidate design. …”

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

“…In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist’s toolbox to enhance the efficiency of both hit optimization and candidate design. …”

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

“…In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist’s toolbox to enhance the efficiency of both hit optimization and candidate design. …”

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

“…In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist’s toolbox to enhance the efficiency of both hit optimization and candidate design. …”

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

“…In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist’s toolbox to enhance the efficiency of both hit optimization and candidate design. …”

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology by Pieter B. Burger (4172578)

“…In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist’s toolbox to enhance the efficiency of both hit optimization and candidate design. …”

Efficient Topology-Optimized Couplers for On-Chip Single-Photon Sources by Omer Yesilyurt (11513040)

“…The fundamental, physics-based intuition gained from this approach could enable the design of high-performance quantum devices.…”

DataSheet1_Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.docx by Sergei Evteev (18294157)

“…</p><p>Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. …”

Face recognition accuracy of distinctive ways. by Yan Liu (25061)

Changes in loss curves of different models. by Yan Liu (25061)