A Graph‐Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules.

Brown, Nathan; McKay, Ben; Gilardoni, François; Gasteiger, Johann

doi:10.1002/chin.200431198

Cited by 46 publications

(78 citation statements)

References 1 publication

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“…Ligand-based de novo drug design, however, is less practiced than structure-based de novo design because of the inherent challenges of accurately evaluating a new molecule in the absence of the receptor structure. To address the challenge of scoring the newly generated molecules, similarity based methods have been applied in addition to QSAR models (Brown et al, 2004). Feher et al (2008) used five selective norepinephrine reuptake inhibitors as a training set to generate 2200 molecules using a combination of structural similarity, 2D pharmacophore similarity, and properties to drive the evolution.…”

Section: Quantitative Structure-activity Relationship Modelsmentioning

confidence: 99%

Computational Methods in Drug Discovery

et al. 2013

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Section: Quantitative Structure-activity Relationship Modelsmentioning

confidence: 99%

Computational Methods in Drug Discovery

et al. 2013

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“…Atom-based algorithms work by mutating atoms, and can therefore fine-tune each structure optimally. This approach has been chosen in many articles [4,8,10,14]. On the other hand, several investigators construct molecules using larger fragments [17,19].…”

Section: The Molecule Representationmentioning

confidence: 99%

The Molecule Evoluator. An Interactive Evolutionary Algorithm for the Design of Drug-Like Molecules

Lameijer

Kok

Bäck

et al. 2006

J. Chem. Inf. Model.

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To help chemists design new drugs, we created a tool that uses interactive evolution to design drug molecules, the "Molecule Evoluator". In contrast to most other evolutionary de novo design programs, the molecule representation and the set of mutations enable it to both search the chemical space of all drug like molecules extensively and to fine-tune molecular structures to the problem at hand. Additionally, we use interaction with the user as a fitness function, which is new in evolutionary algorithms in drug design. This interactivity allows the Molecule Evoluator to use the domain knowledge of the chemist to estimate the ease of synthesis and the biological activity of the compound. This knowledge can guide the optimization process and thereby improve its results. Chemists of our department using the Molecule Evoluator were able to find six novel and synthesizable druglike core structures, indicating that the Molecule Evoluator can be used as a tool to enhance the chemist's creativity.

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“…In an evolutionary approach, new structures may be generated by 'mutating' features of a compound (e.g. changing bond orders, breaking or forming rings, introducing heteroatoms or substituting new atoms) or combining fragments from multiple compounds to create a new, hybrid compound [38]. An alternative method is to use 'medicinal chemistry rules' for modifying compounds, taken from the experience of medicinal chemists, to define compound transformations that may be applied iteratively to create new 'generations' of compounds [39].…”

Section: Multi-objective Evolutionary Algorithmsmentioning

confidence: 99%

Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties

Segall¹

2012

curr drug metab

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A successful, efficacious and safe drug must have a balance of properties, including potency against its intended target, appropriate absorption, distribution, metabolism, and elimination (ADME) properties and an acceptable safety profile. Achieving this balance of, often conflicting, requirements is a major challenge in drug discovery. Approaches to simultaneously optimizing many factors in a design are broadly described under the term 'multi-parameter optimization' (MPO). In this review, we will describe how MPO can be applied to efficiently design and select high quality compounds and describe the range of methods that have been employed in drug discovery, including; simple 'rules of thumb' such as Lipinski's rule; desirability functions; Pareto optimization; and probabilistic approaches that take into consideration the uncertainty in all drug discovery data due to predictive error and experimental variability. We will explore how these methods have been applied to predicted and experimental data to reduce attrition and improve the productivity of the drug discovery process.

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A Graph‐Based Genetic Algorithm and Its Application to the Multiobjective Evolution of Median Molecules.

Cited by 46 publications

References 1 publication

Computational Methods in Drug Discovery

Computational Methods in Drug Discovery

The Molecule Evoluator. An Interactive Evolutionary Algorithm for the Design of Drug-Like Molecules

Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties

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