Genetic optimization of combinatorial libraries

Gobbi, A.; Poppinger, Dieter

doi:10.1002/(sici)1097-0290(199824)61:1<47::aid-bit9>3.0.co;2-z

Cited by 67 publications

(44 citation statements)

References 5 publications

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“…The different levels of fuzziness were controlled by the number of points of the pharmacophore and the shapes of the bins. The fingerprint definition from Gobbi’s work [ 26 ], which is also implemented in RDKit, was used in this study. Table 3 demonstrates the target prediction performances of 3 types of pharmacophore definitions.…”

Section: Resultsmentioning

confidence: 99%

Improving chemical similarity ensemble approach in target prediction

et al. 2016

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BackgroundIn silico target prediction of compounds plays an important role in drug discovery. The chemical similarity ensemble approach (SEA) is a promising method, which has been successfully applied in many drug-related studies. There are various models available analogous to SEA, because this approach is based on different types of molecular fingerprints. To investigate the influence of training data selection and the complementarity of different models, several SEA models were constructed and tested.ResultsWhen we used a test set of 37,138 positive and 42,928 negative ligand-target interactions, among the five tested molecular fingerprint methods, at significance level 0.05, Topological-based model yielded the best precision rate (83.7 %) and (0.784) while Atom pair-based model yielded the best (0.694). By employing an election system to combine the five models, a flexible prediction scheme was achieved with precision range from 71 to 90.6 %, range from 0.663 to 0.684 and range from 0.696 to 0.817.ConclusionsThe overall effectiveness of all of the five models could be ranked in decreasing order as follows: Atom pair Topological > Morgan > MACCS > Pharmacophore. Combining multiple SEA models, which takes advantages of different models, could be used to improve the success rates of the models. Another possibility of improving the model could be using target-specific classes or more active compounds.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0130-x) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Improving chemical similarity ensemble approach in target prediction

et al. 2016

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“…The Morgan captures highly specific atomic information enabling the representation of a large set of precisely defined structural features [60]. Additionally, invariants of Morgan called as FeatMorgan fingerprints can also be calculated by including functional features (i.e., hydrogen-bond donor and acceptors, aromatic, halogen, basic and acid groups) [62].…”

Section: Data Curationmentioning

confidence: 99%

Deep Learning-driven research for drug discovery: Tackling Malaria

Neves

Braga²,

Alves

et al. 2020

PLoS Comput Biol

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Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC 50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates. Author summaryMalaria is a serious infectious disease caused by parasites of the genus Plasmodium. The recommended treatment is a combination of antimalarial drugs. However, the rise of parasites resistant to the current antimalarial drugs means that new therapeutics are continually required. To meet this challenge, we developed and applied models using deep learning, a powerful artificial intelligence method, supported by experimental validation PLOS Computational Biology | https://doi.to identify new drug candidates against malaria. We used the developed computational models to prioritize novel, active, and nontoxic compounds from virtual chemical libraries for experimental evaluation. Then, the predicted antimalarial compounds were experimentally validated in assays on Plasmodium falciparum culture. This allowed us to discover two new potential antimalarial candidates. The use of computational approaches is an attractive route to expedite the discovery of new therapeutics, especially to infectious tropical diseases, as it can reduce time and development costs. Future directions include in vivo studies on animal models.Deep Learning-driven research for tackling Malaria PLOS Computational Biology | https://doi.

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“…ChemTreeMap also calculates atom-pairs fingerprints (Carhart et al, 1985) as an alternative metric. Others can be added easily, such as MACCS keys (Durant et al, 2002), topological torsion fingerprints (Nilakantan et al, 1987) and 2D-pharmacophore fingerprints (Gobbi and Poppinger, 1998).…”

Section: Representation Of the Moleculesmentioning

confidence: 99%

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Lü¹,

Carlson

2016

Bioinformatics

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Motivation: What if you could explain complex chemistry in a simple tree and share that data online with your collaborators? Computational biology often incorporates diverse chemical data to probe a biological question, but the existing tools for chemical data are ill-suited for the very large datasets inherent to bioinformatics. Furthermore, existing visualization methods often require an expert chemist to interpret the patterns. Biologists need an interactive tool for visualizing chemical information in an intuitive, accessible way that facilitates its integration into today's team-based biological research. Results: ChemTreeMap is an interactive, bioinformatics tool designed to explore chemical space and mine the relationships between chemical structure, molecular properties, and biological activity. ChemTreeMap synergistically combines extended connectivity fingerprints and a neighbor-joining algorithm to produce a hierarchical tree with branch lengths proportional to molecular similarity. Compound properties are shown by leaf color, size and outline to yield a user-defined visualization of the tree. Two representative analyses are included to demonstrate ChemTreeMap's capabilities and utility: assessing dataset overlap and mining structure-activity relationships. Availability and Implementation: The examples from this paper may be accessed at http://ajing. github.io/ChemTreeMap/. Code for the server and client are available in the Supplementary Information, at the aforementioned github site, and on Docker Hub (https://hub.docker.com) with the nametag ajing/chemtreemap.

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Genetic optimization of combinatorial libraries

Cited by 67 publications

References 5 publications

Improving chemical similarity ensemble approach in target prediction

Improving chemical similarity ensemble approach in target prediction

Deep Learning-driven research for drug discovery: Tackling Malaria

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

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