From in silico target prediction to multi-target drug design: current databases, methods and applications
Given the tremendous growth of bioactivity databases, the use of computational tools to
predict protein targets of small molecules has been gaining importance in recent years …
predict protein targets of small molecules has been gaining importance in recent years …
Multi-parameter optimization: identifying high quality compounds with a balance of properties
MD Segall - Current pharmaceutical design, 2012 - ingentaconnect.com
A successful, efficacious and safe drug must have a balance of properties, including potency
against its intended target, appropriate absorption, distribution, metabolism, and elimination …
against its intended target, appropriate absorption, distribution, metabolism, and elimination …
Efficient large-scale multiobjective optimization based on a competitive swarm optimizer
There exist many multiobjective optimization problems (MOPs) containing a large number of
decision variables in real-world applications, which are known as large-scale MOPs. Due to …
decision variables in real-world applications, which are known as large-scale MOPs. Due to …
A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization
The current literature of evolutionary many-objective optimization is merely focused on the
scalability to the number of objectives, while little work has considered the scalability to the …
scalability to the number of objectives, while little work has considered the scalability to the …
A grid-based evolutionary algorithm for many-objective optimization
Balancing convergence and diversity plays a key role in evolutionary multiobjective
optimization (EMO). Most current EMO algorithms perform well on problems with two or three …
optimization (EMO). Most current EMO algorithms perform well on problems with two or three …
A survey on multi-objective evolutionary algorithms for many-objective problems
Multi-objective evolutionary algorithms (MOEAs) are well-suited for solving several complex
multi-objective problems with two or three objectives. However, as the number of conflicting …
multi-objective problems with two or three objectives. However, as the number of conflicting …
Local model-based Pareto front estimation for multiobjective optimization
The Pareto front (PF) estimation has become an emerging strategy for solving multiobjective
optimization problems in recent studies. By approximating the geometrical structure of the …
optimization problems in recent studies. By approximating the geometrical structure of the …
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem
ABSTRACT A new data-driven reference vector-guided evolutionary algorithm has been
successfully implemented to construct surrogate models for various objectives pertinent to …
successfully implemented to construct surrogate models for various objectives pertinent to …
A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization
With their complexity and vast search space, large-scale multiobjective optimization
problems (LSMOPs) challenge existing multiobjective evolutionary algorithms (MOEAs) …
problems (LSMOPs) challenge existing multiobjective evolutionary algorithms (MOEAs) …
MOCPSO: A multi-objective cooperative particle swarm optimization algorithm with dual search strategies
Particle swarm optimization (PSO) is a widely embraced meta-heuristic approach to tackling
the complexities of multi-objective optimization problems (MOPs), renowned for its simplicity …
the complexities of multi-objective optimization problems (MOPs), renowned for its simplicity …