.png){kind=icon}
Return
Computer-aided multi-objective optimization in small molecule discovery
.png){kind=icon}
OA
AI
DOI：10.1016/j.patter.2023.100678.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties. Multi-objective molecular design is commonly addressed by combining properties of interest into a single objective function using scalarization, which im-poses assumptions about relative importance and uncovers little about the trade-offs between objectives. In contrast to scalarization, Pareto optimization does not require knowledge of relative importance and reveals the trade-offs between objectives. However, it introduces additional considerations in algorithm design. In this review, we describe pool-based and de novo generative approaches to multi-objective molecular discov-ery with a focus on Pareto optimization algorithms. We show how pool-based molecular discovery is a rela-tively direct extension of multi-objective Bayesian optimization and how the plethora of different generative models extend from single-objective to multi-objective optimization in similar ways using non-dominated sorting in the reward function (reinforcement learning) or to select molecules for retraining (distribution learning) or propagation (genetic algorithms). Finally, we discuss some remaining challenges and opportu-nities in the field, emphasizing the opportunity to adopt Bayesian optimization techniques into multi -objec-tive de novo design.
Keywords:
GENETIC ALGORITHM
DRUG DISCOVERY
DESIGN
GENERATION
IMPLEMENTATION
SOLUBILITY
LIBRARIES
QUALITY
QSAR
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.4
Papers:
954
Citations:
3.6K
Organization
No organization information available
Cited Papers
ShareSave
ShareSaveShareSaveMolecular generative model based on conditional variational autoencoder for de novo molecular designOAAI
OAAIShareSaveAccurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization.png)
ACS CENTRAL SCIENCEOAAI
ACS CENTRAL SCIENCEIF10.4
OAAIShareSaveShareSaveShareSave
View more