Structure‐Based<i>De Novo</i>Drug Design

Reddy, A. Srinivas; Chen, Lu; Zhang, Shuxing

doi:10.1002/9783527677016.ch4

Cited by 5 publications

(2 citation statements)

References 142 publications

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“…Techniques such as virtual screening aim to identify hits from virtual libraries containing large number of molecules, usually by similarity-based searches or by molecular docking 4,6,7 . Another technique is automated molecular generation or automated de novo design where new molecules with specific properties are automatically generated by methods such as structure-based de novo design 8,9 , inverse QSAR 10 , particle swarm optimization 11 , or genetic algorithm 12,13 . Recently, artificial intelligence, in particular generative models, has been extensively used for molecular de novo design, compound optimization and hit identification 14–16 .…”

Section: Introductionmentioning

confidence: 99%

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

et al. 2020

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Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.

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Section: Introductionmentioning

confidence: 99%

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

et al. 2020

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“…Different virtual compound libraries can be designed, depending on the target properties and on the desired pharmacokinetics ( Lionta et al, 2014 ). Therefore, fragment-based and relatively small focused libraries have found great success: a wider chemical space is covered by virtually assembling many different building blocks as in combinatorial synthesis ( Chevillard and Kolb, 2015 ; Reymond, 2015 ) or by building compounds directly starting from the structure complex with the first fragment ( Srinivas Reddy et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Molecular Connectivity Predefines Polypharmacology: Aliphatic Rings, Chirality, and sp3 Centers Enhance Target Selectivity

2017

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Dark chemical matter compounds are small molecules that have been recently identified as highly potent and selective hits. For this reason, they constitute a promising class of possible candidates in the process of drug discovery and raise the interest of the scientific community. To this purpose, Wassermann et al. (2015) have described the application of 2D descriptors to characterize dark chemical matter. However, their definition was based on the number of reported positive assays rather than the number of known targets. As there might be multiple assays for one single target, the number of assays does not fully describe target selectivity. Here, we propose an alternative classification of active molecules that is based on the number of known targets. We cluster molecules in four classes: black, gray, and white compounds are active on one, two to four, and more than four targets respectively, whilst inactive compounds are found to be inactive in the considered assays. In this study, black and inactive compounds are found to have not only higher solubility, but also a higher number of chiral centers, sp3 carbon atoms and aliphatic rings. On the contrary, white compounds contain a higher number of double bonds and fused aromatic rings. Therefore, the design of a screening compound library should consider these molecular properties in order to achieve target selectivity or polypharmacology. Furthermore, analysis of four main target classes (GPCRs, kinases, proteases, and ion channels) shows that GPCR ligands are more selective than the other classes, as the number of black compounds is higher in this target superfamily. On the other side, ligands that hit kinases, proteases, and ion channels bind to GPCRs more likely than to other target classes. Consequently, depending on the target protein family, appropriate screening libraries can be designed in order to minimize the likelihood of unwanted side effects early in the drug discovery process. Additionally, synergistic effects may be obtained by library design toward polypharmacology.

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FBDD & De Novo Drug Design

Das,

Nandi,

Kumari

et al. 2023

Applied Computer-Aided Drug Design: Models and Methods

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Fragment-based drug or lead discovery (FBDD or FBLD) refers to as one of the most significant approaches in the domain of current research in the pharmaceutical industry as well as academia. It offers a number of advantages compared to the conventional drug discovery approach, which include – 1) It needs the lesser size of chemical databases for the development of fragments, 2) A wide spectrum of biophysical methodologies can be utilized for the selection of the best fit fragments against a particular receptor, and 3) It is far more simpler, feasible, and scalable in terms of the application when compared to the classical high-throughput screening methods, making it more popular day by day. For a fragment to become a drug candidate, they are analyzed and evaluated on the basis of numerous strategies and criteria, which are thoroughly explained in this chapter. One important term in the field of FBDD is de novo drug design (DNDD), which means the design and development of new ligand molecules or drug candidates from scratch using a wide range of in silico approaches and algorithmic tools, among which AI-based platforms are gaining large attraction. A principle segment of AI includes DRL that finds numerous applicabilities in the DNDD sector, such as the discovery of novel inhibitors of BACE1 enzyme, identification and optimization of new antagonists of DDR1 kinase enzyme, and development and design of ligand molecules specific to target adenosine A2A, etc. In this book chapter, several aspects of both FBDD and DNDD are briefly discussed.

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Structure‐BasedDe NovoDrug Design

Cited by 5 publications

References 142 publications

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

Molecular Connectivity Predefines Polypharmacology: Aliphatic Rings, Chirality, and sp3 Centers Enhance Target Selectivity

FBDD & De Novo Drug Design

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