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Plan of Potential Development - Engineering
(Macau Talent Program 2018)

Computational Drug Discovery Techniques 計算藥物開發技術

Computational drug discovery is an efficient and effective approach to drug discovery and development process. Due to the dramatic increase in the biological macromolecular structures and small molecule information, the applicability of computational techniques has been widely applied to every stage in the drug development workflow and basic science in drug research. In this course, we will briefly introduce the computational drug discovery process. Through lecturing, demonstration and case study, we will take a closer look at the sequence and molecular information, modeling algorithms, and state-of-the arts machine learning methods with applications on drug discovery. Topics include molecular three-dimensional structure visualization, protein-ligand docking and virtual screening for hit identification, molecular descriptors and classification model for antimicrobial peptides prediction.

計算藥物開發技術為加速及節省藥物開發過程中的有效策略。由於生物大分子和小分子資訊的可用性急劇增加,計算技術已被廣泛應用在藥物開發工作流程中的每一個階段以及在 藥物研究基礎科學上。在本課程中,我們將簡要地介紹計算藥物發現過程。透過教授,演示及案例分析我們將仔細討論序列和分子信息,建模算法,和機器學習方法及其在藥物開發中的應用。主題包括分子三維結構可視化,蛋白質配體對接和虛擬篩選的命中識別方法,以及利用分子描述值和分類模型來預測抗菌肽。

Instructor and Teaching Assistants

Dr. Shirley Siu (shirleysiu at umac dot mo)
Ms. Giotto Tai (mb55502 at umac dot mo)
Mr. Michael Wong
Ms. Jielu Yan

For questions, please address to Shirley or Giotto.

Session Arrangement

All sessions start at 5:30. Each session includes one hour theory (at E11-4025) and one hour hands-on exercise (at E11-1048).

Schedule and Course Content

Date Topic Material
11 Jan 2018 Introduction to SBDD and Protein Structure Theory Lab
18 Jan 2018 Molecular Docking Theory Lab
1 Feb 2018 Treating Protein Flexibility - Docking and MD Theory Lab
8 Feb 2018 Antimicrobial Peptide Prediction by Machine Learning Theory Lab

Software Tools

PyMOL This is a Windows version. Just unzip it and click pymol.exe to execute the pymol.