IADE: a system for intelligent automatic design of bioisosteric analogs

Ertl, Peter; Lewis, Richard A.

doi:10.1007/s10822-012-9609-3

Cited by 24 publications

(26 citation statements)

References 33 publications

(36 reference statements)

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“…In previous works, several authors have highlighted that unrestricted de novo design algorithms can generate compounds which are potentially unstable, reactive, laborious to synthesize, or simply unpleasant to the eye of medicinal chemists. 12,13,75 Systems proposing too many unsuitable compounds lose trust, and will not be accepted by medicinal chemists.…”

Section: Measuring Compound Qualitymentioning

confidence: 99%

GuacaMol: Benchmarking Models for de Novo Molecular Design

Brown

Fiscato

Segler

et al. 2019

J. Chem. Inf. Model.

583

883

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De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural networks appeared recently and show promising results. However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. To standardize the assessment of both classical and neural models for de novo molecular design, we propose an evaluation framework, GuacaMol, based on a suite of standardized benchmarks. The benchmark tasks encompass measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multi-objective optimization tasks. The benchmarking open-source Python code, and a leaderboard can be found on https://benevolent.ai/guacamol.

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Section: Measuring Compound Qualitymentioning

confidence: 99%

GuacaMol: Benchmarking Models for de Novo Molecular Design

Brown

Fiscato

Segler

et al. 2019

J. Chem. Inf. Model.

583

883

View full text Add to dashboard Cite

show abstract

“…Likewise, SBDD studies frequently employ virtual screening procedures using a process commonly known as ligand-receptor docking [8,30–32]. LBDD and SBDD methods remain the subject of intensive research to improve their speed, accuracy, and sophistication [33,34]. …”

Section: Introductionmentioning

confidence: 99%

In silico design of anti-atherogenic biomaterials

et al. 2013

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Atherogenesis, the uncontrolled deposition of modified lipoproteins in inflamed arteries, serves as a focal trigger of cardiovascular disease (CVD). Polymeric biomaterials have been envisioned to counteract atherogenesis based on their ability to repress scavenger mediated uptake of oxidized lipoprotein (oxLDL) in macrophages. Following the conceptualization in our laboratories of a new library of amphiphilic macromolecules (AMs), assembled from sugar backbones, aliphatic chains and poly(-ethylene glycol) tails, a more rational approach is necessary to parse the diverse features such as charge, hydrophobicity, sugar composition and stereochemistry. In this study, we advance a computational biomaterials design approach to screen and elucidate anti-atherogenic biomaterials with high efficacy. AMs were quantified in terms of not only 1D (molecular formula) and 2D (molecular connectivity) descriptors, but also new 3D (molecular geometry) descriptors of AMs modeled by coarse-grained molecular dynamics (MD) followed by all-atom MD simulations. Quantitative structure-activity relationship (QSAR) models for anti-atherogenic activity were then constructed by screening a total of 1164 descriptors against the corresponding, experimentally measured potency of AM inhibition of oxLDL uptake in human monocyte-derived macrophages. Five key descriptors were identified to provide a strong linear correlation between the predicted and observed anti-atherogenic activity values, and were then used to correctly forecast the efficacy of three newly designed AMs. Thus, a new ligand-based drug design framework was successfully adapted to computationally screen and design biomaterials with cardiovascular therapeutic properties.

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“…[67][68][69][70][71] Alternatively, molecules are built by assembling molecular fragment rather than single atoms. [72][73][74][75][76][77][78][79][80][81][82][83] While the use of multi-atom building blocks implies a low resolution in the exploration of the chemical space, 84 it also allows to control the type of functionalities generated and to avoid most of the unrealistic candidates. The combination of selected molecular fragment and connection rules efficiently confines the chemical space according to the specific needs and defines a subspace referred as to the fragment space.…”

Section: Building Blocks: Atoms Vs Fragmentsmentioning

confidence: 99%

Automated Design of Realistic Organometallic Molecules from Fragments

Foscato

Occhipinti

Venkatraman

et al. 2014

J. Chem. Inf. Model.

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A method for the automated generation of realistic, synthetically accessible transition metal and organometallic complexes is described. Computational tools were designed to generate molecular fragments, preferably harvested from libraries of existing, stable compounds, to be used as building blocks for the construction of new molecules. These fragments are enriched with information about the number and type of possible connections to other fragments and are stored in library files. When connecting fragments in the subsequent building process, compatibility matrices, which define the connection rules between fragments, are used to delineate organometallic fragment spaces from which molecules can be generated in an automated fashion. The approach is flexible and allows ample structural variation at the same time as the combination of known fragments is easily restrained to avoid generation of exotic and unrealistic substructures and molecules. The method was tested in the generation of ruthenium complexes, with a given coordination environment, which can serve as candidates in catalyst development. The results demonstrate that molecules generated with the described method do not contain exotic arrangements of atoms and are by far more realistic than those obtained by the application of valence rules alone.

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IADE: a system for intelligent automatic design of bioisosteric analogs

Cited by 24 publications

References 33 publications

GuacaMol: Benchmarking Models for de Novo Molecular Design

GuacaMol: Benchmarking Models for de Novo Molecular Design

In silico design of anti-atherogenic biomaterials

Automated Design of Realistic Organometallic Molecules from Fragments

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