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"ABSTRAKInhibitor Dipeptidyl Peptidase-4 (DPP-4) menjadi obat yang semakin penting dalam pengobatan diabetes melitus tipe-2, namun beberapa golongan obat ini memiliki efek samping seperti nyeri sendi yang bisa menjadi parah hingga pankreatitis, diperkirakan efek samping ini muncul terkait dengan penghambatannya terhadap enzim DPP-8 dan DPP-9. Untuk mengembangankan inhibitor DPP-4 baru yang memiliki aktivitas penghambatan yang tinggi terhadap DPP-4 dan penghambatan yang rendah terhadap DPP-8 dan DPP-9 maka dilakukan virtual screening pada lebih dari 10 juta molekul, dengan membangun workflow virtual screening menggunakan metode Quantitative Structure-Activity Relationship (QSAR) berbasis artificial intelligence (AI). Lima algoritma machine learning regresi dan empat algoritma machine learning klasifikasi digunakan untuk membangun workflow virtual screening. Algoritma yang memenuhi syarat untuk model QSAR regresi adalah Support Vector regression dengan R2pred 0,78 sedangkan model QSAR klasifikasi adalah Random Forest dengan akurasi 92,21%. Dari hasil virtual screening didapatkan senyawa hit dengan pIC50 diatas 7,5 sebanyak 2.716 senyawa. Hasil penambatan molekul beberapa senyawa hit ke enzim DPP-4, DPP-8 dan DPP-9, didapatkan senyawa hit potensial adalah senyawa CH0002. Senyawa hit ini dapat dikembangkan lebih lanjut sebagai inhibitor DPP-4 dan workflow virtual screening pada penelitian ini dapat diterapkan pada target lainnya.

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ABSTRACTDipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an important drugs in the treatment of type 2 diabetes mellitus, but some classes of these drugs have side effects such as joint pain that can become severe to pancreatitis, these side effects appear to related with their inhibition against other DPP enzymes. This study aims to find DPP-4 inhibitor hit compounds that are selective against DPP-8 and DPP-9 enzymes. virtual screening is carried out on more than 10 million molecules, by building a virtual screening workflow using Quantitative Structure-Activity Relationship (QSAR) method based on Artificial Intelligence (AI). Five regression algortihms and four classification algorithms machine learning were used to build virtual screening workflows. The algorithm that qualifies for the QSAR regression model was Support Vector regression with R2pred 0,78 while the classification QSAR model was Random Forest with an accuracy of 92,21%. Results of virtual screening obtained hit compounds with pIC50 above 7,5 were 2.716 compounds. Results of molecular docking from several hit compounds to the enzymes DPP-4, DPP-8 and DPP-9, potential hit compound was CH0002. This hit compound can be further developed as a DPP-4 inhibitor and virtual screening workflow in this study can be applied to other targets."

"Summary:This book describes some of the most exciting developments for the discovery of new drugs, such as Fragment-based methods. This book includes experimental approaches using X-ray crystallography and NMR for Fragment-based screening as well as other biophysical methods for studying protein/ligand interactions"

"Presenting an overview of chiral drugs and their impact on the pharmaceutical industry, Chiral drugs : chemistry and biological action provides an integrated perspective of chiral drugs from concept, synthesis, and pharmaceutical properties. The book includes important chiral technologies and reviews of 200 chiral drugs that have either been approved or made it to advanced trials. It's interdisciplinary approach combines synthetic organic chemistry, medicinal chemistry, and pharmacology in a way that fosters cooperation among interdisciplinary scientists and researchers in both academia and the pharma or biotech industries."

"Drug discovery is all about finding small molecules that interact in a desired way with larger molecules, namely proteins and other macromolecules in the human body. If the three-dimensional structures of both the small and large molecule are known, their interaction can be tested by computer simulation with a reasonable degree of accuracy. Alternatively, if active ligands are already available, molecular similarity searches can be used to find new molecules. This virtual screening can even be applied to compounds that have yet to be synthesized, as opposed to "real" screening that requires cost- and labor-intensive laboratory testing with previously synthesized drug compounds. Unique in its focus on the end user, this is a real "how to" book that does not presuppose prior experience in virtual screening or a background in computational chemistry. It is both a desktop reference and practical guide to virtual screening applications in drug discovery, offering a comprehensive and up-to-date overview. Clearly divided into four major sections, the first provides a detailed description of the methods required for and applied in virtual screening, while the second discusses the most important challenges in order to improve the impact and success of this technique. The third and fourth, practical parts contain practical guidelines and several case studies covering the most important scenarios for new drug discovery, accompanied by general guidelines for the entire workflow of virtual screening studies. Throughout the text, medicinal chemists from academia, as well as from large and small pharmaceutical companies report on their experience and pass on priceless practical advice on how to make best use of these powerful methods."