Sources for Leads: Natural Products and Libraries

Herwerden, Eric F. van; Süssmuth, Roderich D.

doi:10.1007/164_2015_19

Cited by 11 publications

(7 citation statements)

References 125 publications

(100 reference statements)

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“…It is, of course, well known that many natural products can serve as medicines [88,237], and that many purified substances derived therefrom are the basis of a significant fraction -probably 35-60 % depending on how one counts -of marketed drugs (see above, and [158,[238][239][240]). We think that the present work highlights even more clearly how important natural products and their derivatives are likely to be in terms of producing novel, safe and efficacious drugs.…”

Section: Drugs and Natural Productsmentioning

confidence: 99%

Consensus rank orderings of molecular fingerprints illustrate the most genuine similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

O’Hagan¹,

Kell

2017

ADMET DMPK

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We compare several molecular fingerprint encodings for marketed, small molecule drugs, and assess how their rank order varies with the fingerprint in terms of the Tanimoto similarity to the most similar endogenous human metabolite as taken from Recon2. For the great majority of drugs, the rank order varies very greatly depending on the encoding used, and also somewhat when the Tanimoto similarity (TS) is replaced by the Tversky similarity. However, for a subset of such drugs, amounting to some 10 % of the set and a Tanimoto similarity of ~0.8 or greater, the similarity coefficient is relatively robust to the encoding used. This leads to a metric that, while arbitrary, suggests that a Tanimoto similarity of 0.75-0.8 or greater genuinely does imply a considerable structural similarity of two molecules in the drug-endogenite space. Although comparatively few (<10 % of) marketed drugs are, in this sense, robustly similar to an endogenite, there is often at least one encoding with which they are genuinely similar (e.g. TS > 0.75). This is referred to as the Take Your Pick Improved Cheminformatic Analytical Likeness or TYPICAL encoding, and on this basis some 66 % of drugs are within a TS of 0.75 to an endogenite.We next explicitly recognise that natural evolution will have selected for the ability to transport dietary substances, including plant, animal and microbial ‘secondary’ metabolites, that are of benefit to the host. These should also be explored in terms of their closeness to marketed drugs. We thus compared the TS of marketed drugs with the contents of various databases of natural products. When this is done, we find that some 80 % of marketed drugs are within a TS of 0.7 to a natural product, even using just the MACCS encoding. For patterned and TYPICAL encodings, 80 % and 98 % of drugs are within a TS of 0.8 to (an endogenite or) an exogenous natural product. This implies strongly that it is these exogeneous (dietary and medicinal) natural products that are more to be seen as the ‘natural’ substrates of drug transporters (as is recognised, for instance, for the solute carrier SLC22A4 and ergothioneine). This novel analysis casts an entirely different light on the kinds of natural molecules that are to be seen as most like marketed drugs, and hence potential transporter substrates, and further suggests that a renewed exploitation of natural products as drug scaffolds would be amply rewarded.

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Section: Drugs and Natural Productsmentioning

confidence: 99%

Consensus rank orderings of molecular fingerprints illustrate the most genuine similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

O’Hagan¹,

Kell

2017

ADMET DMPK

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“…Akin to many scientific discoveries, AI in drug discovery has harbored healthy skepticism among the medicinal chemists. It is yet to be tested how AI can tackle the challenge of imbibing natural products to build synthetically feasible de novo designs as effective pharmacophores [ 67 , 68 ]. Also, the ventured chemical space has largely been small molecules; however, the advent of bio-conjugate therapies such as immunotherapy, RNAi therapeutics etc., where chemistry converges with biology is yet to be explored.…”

Section: Artificial Intelligence (Ai) In Drug Design and Molecularmentioning

confidence: 99%

Deep Learning in Drug Discovery and Medicine; Scratching the Surface

et al. 2018

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The practice of medicine is ever evolving. Diagnosing disease, which is often the first step in a cure, has seen a sea change from the discerning hands of the neighborhood physician to the use of sophisticated machines to use of information gleaned from biomarkers obtained by the most minimally invasive of means. The last 100 or so years have borne witness to the enormous success story of allopathy, a practice that found favor over earlier practices of medical purgatory and homeopathy. Nevertheless, failures of this approach coupled with the omics and bioinformatics revolution spurred precision medicine, a platform wherein the molecular profile of an individual patient drives the selection of therapy. Indeed, precision medicine-based therapies that first found their place in oncology are rapidly finding uses in autoimmune, renal and other diseases. More recently a new renaissance that is shaping everyday life is making its way into healthcare. Drug discovery and medicine that started with Ayurveda in India are now benefiting from an altogether different artificial intelligence (AI)—one which is automating the invention of new chemical entities and the mining of large databases in health-privacy-protected vaults. Indeed, disciplines as diverse as language, neurophysiology, chemistry, toxicology, biostatistics, medicine and computing have come together to harness algorithms based on transfer learning and recurrent neural networks to design novel drug candidates, a priori inform on their safety, metabolism and clearance, and engineer their delivery but only on demand, all the while cataloging and comparing omics signatures across traditionally classified diseases to enable basket treatment strategies. This review highlights inroads made and being made in directed-drug design and molecular therapy.

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“…Natural products have always inspired medicinal chemistry . With the massive renewed interest in natural products as sources for leads and inspiration for drug discovery, de novo molecular design approaches face the challenge of converting a potentially structurally complex or uncommon natural product structure into a synthetically accessible druglike compound. There is a rich history of peptidomimetics in de novo drug design. − However, we feel that computational design has potential for more.…”

Section: What Does the Future Hold?mentioning

confidence: 99%

De Novo Design at the Edge of Chaos

2016

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Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.

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Sources for Leads: Natural Products and Libraries

Cited by 11 publications

References 125 publications

Consensus rank orderings of molecular fingerprints illustrate the most genuine similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

Consensus rank orderings of molecular fingerprints illustrate the most genuine similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

Deep Learning in Drug Discovery and Medicine; Scratching the Surface

De Novo Design at the Edge of Chaos

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