ChemistGA: a chemical synthesizable accessible molecular generation algorithm for real-world drug discovery
Many deep learning (DL)-based molecular generative models have been proposed to
design novel molecules. These models may perform well on benchmarks, but they usually …
design novel molecules. These models may perform well on benchmarks, but they usually …
ChemSpaceAL: An efficient active learning methodology applied to protein-specific molecular generation
The incredible capabilities of generative artificial intelligence models have inevitably led to
their application in the domain of drug discovery. It is therefore of tremendous interest to …
their application in the domain of drug discovery. It is therefore of tremendous interest to …
[HTML][HTML] Designing mechanosensitive molecules from molecular building blocks: A genetic algorithm-based approach
Single molecules can be used as miniaturized functional electronic components, when
contacted by macroscopic electrodes. Mechanosensitivity describes a change in …
contacted by macroscopic electrodes. Mechanosensitivity describes a change in …
Inverse design of copolymers including stoichiometry and chain architecture
The demand for innovative synthetic polymers with improved properties is high, but their
structural complexity and vast design space hinder rapid discovery. Machine learning …
structural complexity and vast design space hinder rapid discovery. Machine learning …
Development of Deep Learning approaches to predict relationships between chemical structures and sweetness
The non-caloric sweeteners market is catching up with the market of conventionally used
sugars due to the benefits of preventing obesity, tooth decay and other health problems …
sugars due to the benefits of preventing obesity, tooth decay and other health problems …
A Generative Evolutionary Many-Objective Framework: A Case Study in Antimicrobial Agent Design
de novo drug design (dnDD) aims to generate novel molecules that meet several conflicting
objectives, positioning it as a quintessential many-objective optimization problem (MaOP) …
objectives, positioning it as a quintessential many-objective optimization problem (MaOP) …
Molecule optimization via multi-objective evolutionary in implicit chemical space
X **a, Y Su, C Zheng, X Zeng - arxiv preprint arxiv:2212.08826, 2022 - arxiv.org
Machine learning methods have been used to accelerate the molecule optimization process.
However, efficient search for optimized molecules satisfying several properties with scarce …
However, efficient search for optimized molecules satisfying several properties with scarce …
AI Enabled Drug Design and Side Effect Prediction Powered by Multi-Objective Evolutionary Algorithms & Transformer Models
K Grantham - 2023 - dr.library.brocku.ca
Due to the large search space and conflicting objectives, drug design and discovery is a
difficult problem for which new machine learning (ML) approaches are required. Here, the …
difficult problem for which new machine learning (ML) approaches are required. Here, the …