@article { ISI:000366429100003, title = {Breaking and making of rings: a method for the preparation of 4-quinolone-3-carboxylic acid amides and the expensive drug ivacaftor}, journal = {European Journal of Organic Chemistry}, number = {34}, year = {2015}, month = {DEC}, pages = {7433-7437}, publisher = {WILEY-V C H VERLAG GMBH}, address = {POSTFACH 101161, 69451 WEINHEIM, GERMANY}, abstract = {

A simple and convenient method to access 4-quinolone-3-carboxylic acid amides from indole-3-acetic acid amides through one-pot oxidative cleavage of the indole ring followed by condensation (Witkop-Winterfeldt type oxidation) was explored. The scope of the method was confirmed with more than 20 examples and was successfully applied to the synthesis of the drug Ivacaftor, the most expensive drug on the market.

}, keywords = {Amides, Drug design, Fused-ring systems, Oxidation, Ozonolysis}, issn = {1434-193X}, doi = {10.1002/ejoc.201501048}, author = {Vasudevan, N. and Jachak, Gorakhnath R. and Reddy, D. Srinivasa} } @article { ISI:000360934800005, title = {Chemoinformatics approach for building molecular networks from marine organisms}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {7}, year = {2015}, month = {JAN}, pages = {673-684}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

Natural products obtained from marine sources are considered to be a rich and diverse source of potential drugs. In the present work we demonstrate the use of chemoinformatics approach for the design of new molecules inspired by molecules from marine organisms. Accordingly we have assimilated information from two major scientific domains namely chemoinformatics and biodiversity informatics to develop an interactive marine database named MIMMO (Medicinally Important Molecules from Marine Organisms). The database can be queried for species, molecules, scaffolds, drugs, diseases and associated cumulative biological activity spectrum along with links to the literature resources. Molecular informatics analysis of the molecules obtained from MIMMO was performed to study their chemical space. The distinct skeletal features of the biologically active compounds isolated from marine species were identified. Scaffold molecules and species networks were created to identify common scaffolds from marine source and drug space. An analysis of the entire molecular data revealed a unique list of around 2000 molecules from which ten most frequently occurring distinct scaffolds were obtained.

}, keywords = {Chemoinformatics, Drug design, marine, virtual library}, issn = {1386-2073}, doi = {10.2174/1386207318666150703112950}, author = {Karthikeyan, Muthukumarasamy and Nimje, Deepika and Pahujani, Rakhi and Tyagi, Kushal and Bapat, Sanket and Vyas, Renu and Padmakumar, Krishna Pillai} } @article {47392, title = {Machine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics}, journal = {Bioorganic \& Medicinal Chemistry Letters}, volume = {38}, year = {2021}, month = {APR }, pages = {127852}, type = {Article}, abstract = {

De novo design of mini-proteins (4-12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. In this study we trained machine learning classifiers which can distinguish, with 90\% accuracy and 80\% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.

}, keywords = {Drug design, machine learning, Mini-proteins, Protein therapeutics, virtual screening}, issn = {0960-894X}, doi = {10.1016/j.bmcl.2021.127852}, author = {Gaur, Neeraj K. and Goyal, Venuka Durani and Kulkarni, Kiran and Makde, Ravindra D.} }