IN SILICO METHODOLOGIES FOR SELECTION AND PRIORITIZATION OF COMPOUNDS IN DRUG DISCOVERY YEO WEE KIANG (M.Sc. (Bioinformatics), NTU) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF PHARMACY NATIONAL UNIVERSITY OF SINGAPORE 2012 DECLARATION I hereby declare that this thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. Yeo Wee Kiang 10th September 2012 i ACKNOWLEDGEMENTS It is a great pleasure to acknowledge the support that I have received during my doctoral research. First, I must express my heartfelt gratitude to my academic supervisor at the National University of Singapore, Associate Professor Go Mei Lin for her patience, guidance and the opportunity to be part of her research group. Her receptiveness to novel ideas and her research experience has provided me both the freedom to explore as well as the delicate environment where new ideas can be incubated without premature reprisal. In spite of her many commitments, she has always been approachable and generous with her time. From time to time, I wonder how she sustains her constantly high energy levels and neverending enthusiasm. She is a ready role model for how an Investigator and mentor should be. Indeed, it is my good fortune to have Prof Go as my academic supervisor. My sincere appreciation also goes to Dr Shahul Nilar, my industry supervisor at the Novartis Institute for Tropical Diseases (NITD) Computational Chemistry team, for imbuing me with copious amounts of optimism amidst the trials and tribulations of industrial drug discovery. His continuous encouragement, critique and guidance have been instrumental to my work. Most importantly, he has inculcated in me the value of healthy scepticism and imparted the ‘thinking’ approach to conducting innovative research. Dr Nilar achieved that by providing abundant ‘space’ for me to tinker with alternative methods to solve problems instead of merely shoving down a dogmatic solution. ii It was during my days as a graduate student that I experienced the unbelievable power of conceptual combination and morphological analysis. Hence I am now able to appreciate their contributions to problem-solving and their roles in innovation. Indeed, I am grateful that both of my supervisors have given me the opportunity to experience the joy and exhilaration of scientific discovery. I would also like to thank Dr Thomas Keller, former Head of Chemistry Unit at NITD for his guidance and the opportunity to work in the lively community of more than 100 international researchers at NITD. I am grateful to Dr Paul Smith, Head of Chemistry at NITD, for providing critical suggestions that sharpened my work. Also, I would like to thank Dr Ida Ma for providing expertise critique of my projects and the corresponding manuscripts. Next, I am indebted to Dr Lim Siew Pheng and Dr Chen Yen-Liang and their teams at the NITD Disease Biology Unit for performing the Dengue RNA dependent RNA polymerase assays and for sharing their knowledge on the enzyme. I would also like to thank Dr David Beer and his team, NITD Screening Unit, who conducted the primary and reconfirmation screens that I have used for the compound selection and prioritization aspect of my research work. My sincere gratitude also goes to Mr Koh Siang Boon and Ms Meg Tan Kheng Lin who put in enormous effort to synthesize the compounds for the Taguchi method section of my research work. In particular, they conducted the corresponding biological assays that were instrumental to the validation of the method. iii I am also grateful to my friends, colleagues, lab-mates and fellow graduate students (some of whom have since graduated):  Ms Meera Gurumurthy, Ms Pramila Ghode, Ms Michelle Lim, Ms Pearly Ng, Ms Gladys Lee, Mr Ian Heng and Ms Aznilah Lathiff from NITD;  Dr Jenefer Alam and Ms Ngew Xinyi formerly from NITD;  Dr Low Kai Leng formerly from Department of Biochemistry, NUS;  Dr Zhang Wei, Dr Leow Jo Lene, Dr Lee Chong Yew, Dr Sim Hong May, Dr Nguyen Thi Hanh Thuy, Dr Wee Xi Kai, Mr Pondy Murgappan Ramanujulu, Ms Chen Xiao, Ms Meg Tan Kheng Lin, Ms Xu Jin, Mr Sherman Ho, Ms Sim Mei Yi, Ms Yap Siew Qi, Dr Suresh Kumar Gorla and Dr Yang Tianming, from Assoc Prof Go’s lab group in the Department of Pharmacy, NUS; The PhD scholarship from NITD is hereby gratefully acknowledged. Besides financial support for my tuition fees, it has funded me generously to attend international conferences that provided the precious opportunities to meet and interact with eminent colleagues abroad. Without such big-hearted support, international conferences would have been out of reach for graduate students like me. In all, Novartis has offered me exceptional opportunities for realworld insights into the science, technology and highly collaborative nature of modern drug discovery in the pharmaceutical industry. iv PUBLICATIONS & CONFERENCES This thesis is based on the following papers (listed in chronological order of the date of publication), manuscripts and other unpublished data: Publications 1. Wee Kiang Yeo, Kheng Lin Tan, Siang Boon Koh, Matiullah Khan, Shahul H. Nilar and Mei Lin Go. Exploration and Optimization of Structure–Activity Relationships in Drug Design using the Taguchi Method. ChemMedChem, 2012, 7, 977-982. 2. Wee Kiang Yeo, Mei Lin Go and Shahul H. Nilar. Extraction and validation of substructure profiles for enriching compound libraries. Journal of Computer-Aided Molecular Design, 2012, accepted for publication. Manuscripts in preparation 1. Wee Kiang Yeo, Thomas H. Keller, Mei Lin Go and Shahul H. Nilar. A novel approach to compound selection and prioritization for hits from High-Throughput Screening campaigns. Manuscript in preparation. 2. Wee Kiang Yeo, Chin Chin Lim, Feng Gu, Yen-Liang Chen, Siew Pheng Lim, Mei Lin Go and Shahul H. Nilar. Multistep virtual screening for identification of non-nucleoside inhibitors of dengue RNA-dependent RNA polymerase. Manuscript in preparation. The following papers were published in the course of the Ph.D. study but not form part of this thesis: v 1. Xi Kai Wee, Wee Kiang Yeo, Bing Zhang, Vincent B.C. Tan, Kian Meng Lim, Tong Earn Tay and Mei Lin Go. Synthesis and evaluation of functionalized isoindigos as antiproliferative agents. Bioorganic & Medicinal Chemistry, 2009, 17, 7562-7571. 2. Kai Leng Low, Guanghou Shui, Klaus Natter, Wee Kiang Yeo, Sepp D. Kohlwein, Thomas Dick, P.S. Srinivasa Rao and Markus R. Wenk. Lipid droplet-associated proteins are involved in the biosynthesis and hydrolysis of triacylglycerol in Mycobacterium bovis Bacillus Calmette-Guérin. Journal of Biological Chemistry, 2010, 285, 21662-21670. 3. Hong May Sim, Ker Yun Loh, Wee Kiang Yeo, Chong Yew Lee and Mei Lin Go. Aurones as modulators of ABCG2 and ABCB1: Synthesis and Structure-activity relationships. ChemMedChem, 2011, 6, 713-724. CONFERENCE PRESENTATIONS (ORAL) 1. 11th Asia Pacific Rim Universities (APRU) Doctoral Students Conference (12th to 16th July, 2010, Jakarta, Indonesia): Research for the Sustainability of Civilization in Pacific Rim: Past, Present and Future. Oral presentation title: “Expediting the lead optimization phase of drug discovery using ‘Design of Experiments’ methods”. 2. 6th American Association of Pharmaceutical Scientists-National University of Singapore (AAPS-NUS) Student Chapter Scientific Symposium (7th April 2010, Singapore). Oral presentation title: “A novel approach to compound selection and prioritization for hits from High-Throughput Screening campaigns”. vi CONFERENCE PRESENTATIONS (POSTER) 1. 7th American Association of Pharmaceutical Scientists-National University of Singapore (AAPS-NUS) Student Chapter Pharmsci@Asia Symposium (6th June 2012, Singapore): Exploring Pharmaceutical Sciences: New Challenges & Opportunities. Poster title: “Extraction and validation of substructure profiles for enriching compound libraries”. 2. Annual National University of Singapore Pharmacy Symposium 2012 (4th April 2012, Singapore). Poster title: “Exploration and Optimization of Structure–Activity Relationships in Drug Design using the Taguchi Method”. 3. Gordon Research Conference on Computer-Aided Drug Design 2011 (17th – 22nd July 2011, Mount Snow Resort, West Dover, Vermont, United States of America). Poster title: “A Random Forest Clustering Approach to Compound Selection and Prioritization for High-Throughput Screening Campaigns”. 4. The 7th International Symposium for Chinese Medicinal Chemists (1st-5th February 2010, Kaohsiung, Republic of China). Poster title: “Virtual screening of small-molecule libraries against dengue RNAdependent RNA polymerase”. 5. UK-Singapore Symposium on Medicinal Chemistry 2010 (25th – 26th January 2010, Biopolis, Singapore). vii Poster title: “Virtual screening of small-molecule libraries against dengue RNAdependent RNA polymerase”. 6. Molecular Modelling 2009: Molecular Modelling from Dynamical, Bio-molecular and Materials Nanotechnology Perspectives (26th-29th July 2009, Gold Coast, Australia). Poster title: “Virtual screening of small-molecule libraries against dengue RNAdependent RNA polymerase”. viii TABLE OF CONTENTS DECLARATION i ACKNOWLEDGEMENTS .ii PUBLICATIONS & CONFERENCES . v Conference presentations (Oral) . vi Conference presentations (Poster) .vii TABLE OF CONTENTS ix SUMMARY xi LIST OF TABLES . xiii LIST OF FIGURES .xvii LIST OF ABBREVIATIONS xx CHAPTER DISCOVERY INTRODUCTION TO COMPUTATIONAL METHODS IN DRUG 1.1 Introduction 1.2 Virtual Screening . 1.3 Molecular Docking & Scoring Functions 1.4 Molecular Similarity 1.5 Pharmacophores . 1.6 Substructure Searching 1.7 Machine Learning in Virtual Screening . 11 1.8 Statement of Purpose . 13 CHAPTER HIGH THROUGHPUT SCREENING HIT LIST TRIAGING . 16 2.1 Introduction 16 2.2 Materials and Methods . 23 2.2.1 Datasets 23 2.2.2 Pre-processing 24 2.2.3 Decision Stump 25 2.2.4 Random Forest Clustering . 26 2.2.5 Descriptor Selection . 27 2.3 Results and Discussion 31 2.3.1 Performance of Random Forest Clustering, Decision Stump versus µ+3σ Method using 14 descriptors . 31 2.3.2 Performance of Random Forest Clustering using Hopkins-based selected descriptors versus 14 descriptors . 42 ix BIBLIOGRAPHY 217. 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Rank EGFR Validation Set EGFR Validation Set EGFR Validation Set EGFR Validation Set EGFR Validation Set EGFR Test Set SRC Validation Set SRC Validation Set SRC Validation Set SRC Validation Set SRC Validation Set SRC Test Set AKT1 Validation Set AKT1 Validation Set AKT1 Validation Set AKT1 Validation Set AKT1 Validation Set AKT1 Test Set PKCβ Validation Set PKCβ Validation Set PKCβ Validation Set PKCβ Validation Set PKCβ Validation Set PKCβ Test Set CDK2 Validation Set CDK2 Validation Set CDK2 Validation Set CDK2 Validation Set CDK2 Validation Set CDK2 Test Set p38α Validation Set p38α Validation Set p38α Validation Set p38α Validation Set p38α Validation Set p38α Test Set 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP491 PubchemFP438 PubchemFP386 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP491 PubchemFP386 PubchemFP438 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP491 PubchemFP386 PubchemFP438 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP385 PubchemFP572 PubchemFP386 PubchemFP491 PubchemFP438 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP438 PubchemFP491 PubchemFP386 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP378 PubchemFP572 PubchemFP385 PubchemFP491 PubchemFP386 PubchemFP438 PubchemFP447 PubchemFP674 PubchemFP577 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP16 PubchemFP445 PubchemFP484 PubchemFP674 PubchemFP443 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP16 PubchemFP484 PubchemFP445 PubchemFP674 PubchemFP443 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP16 PubchemFP484 PubchemFP445 PubchemFP674 PubchemFP443 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP530 PubchemFP16 PubchemFP445 PubchemFP443 PubchemFP528 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP16 PubchemFP484 PubchemFP445 PubchemFP674 PubchemFP443 PubchemFP621 PubchemFP491 PubchemFP447 PubchemFP577 MACCSFP49 PubchemFP530 PubchemFP16 PubchemFP484 PubchemFP445 PubchemFP180 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP385 PubchemFP372 PubchemFP445 PubchemFP491 PubchemFP577 PubchemFP16 MACCSFP154 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP491 PubchemFP445 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP445 PubchemFP491 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP445 PubchemFP491 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP491 PubchemFP445 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP49 PubchemFP261 PubchemFP258 PubchemFP372 PubchemFP385 PubchemFP445 PubchemFP491 PubchemFP577 PubchemFP16 PubchemFP403 MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 PubchemFP431 MACCSFP104 PubchemFP403 MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 MACCSFP104 PubchemFP431 PubchemFP403 MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 PubchemFP431 MACCSFP104 PubchemFP403 MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 MACCSFP104 PubchemFP346 None MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 MACCSFP104 PubchemFP145 PubchemFP403 None MACCSFP89 PubchemFP150 PubchemFP712 PubchemFP597 PubchemFP528 PubchemFP576 PubchemFP145 MACCSFP104 PubchemFP431 PubchemFP403 PubchemFP621 PubchemFP379 PubchemFP530 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP596 PubchemFP16 PubchemFP674 PubchemFP521 PubchemFP621 PubchemFP379 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP674 PubchemFP521 PubchemFP482 PubchemFP621 PubchemFP379 PubchemFP145 PubchemFP530 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP576 PubchemFP482 MACCSFP106 PubchemFP621 PubchemFP379 PubchemFP530 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP521 PubchemFP482 PubchemFP621 PubchemFP379 PubchemFP530 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP521 PubchemFP482 PubchemFP621 PubchemFP379 PubchemFP530 PubchemFP145 PubchemFP435 PubchemFP357 PubchemFP16 PubchemFP596 PubchemFP521 PubchemFP482 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 MACCSFP62 PubchemFP636 MACCSFP107 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 PubchemFP577 MACCSFP62 PubchemFP636 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 MACCSFP62 PubchemFP577 PubchemFP636 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 PubchemFP577 MACCSFP62 PubchemFP636 PubchemFP373 PubchemFP674 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP491 PubchemFP379 MACCSFP62 PubchemFP577 PubchemFP636 PubchemFP674 PubchemFP373 PubchemFP372 PubchemFP435 PubchemFP445 PubchemFP379 PubchemFP491 MACCSFP62 PubchemFP577 PubchemFP636 160 APPENDICES Table S1.2 The cumulative percentage of potent compounds picked up (by PubChem-MACCS fingerprint keys) at each decile. Decile 10 EGFR Validation Set 49.7% 75.5% 85.2% 87.7% 89.0% 93.4% 100.0% 100.0% 100.0% 100.0% EGFR Validation Set 52.4% 78.0% 83.7% 87.2% 88.5% 97.8% 100.0% 100.0% 100.0% 100.0% EGFR Validation Set 52.3% 75.9% 85.1% 87.0% 87.6% 89.8% 100.0% 100.0% 100.0% 100.0% EGFR Validation Set 51.4% 78.5% 85.2% 86.8% 88.3% 89.6% 100.0% 100.0% 100.0% 100.0% EGFR Validation Set 52.4% 77.8% 85.4% 87.9% 88.6% 89.8% 100.0% 100.0% 100.0% 100.0% EGFR Test Set 52.4% 73.7% 84.3% 87.1% 87.8% 90.0% 100.0% 100.0% 100.0% 100.0% SRC Validation Set 60.8% 81.6% 88.5% 97.7% 99.5% 99.5% 100.0% 100.0% 100.0% 100.0% SRC Validation Set 56.2% 74.2% 84.3% 90.8% 97.2% 98.2% 100.0% 100.0% 100.0% 100.0% SRC Validation Set 65.4% 81.1% 91.7% 92.6% 94.0% 100.0% 100.0% 100.0% 100.0% 100.0% SRC Validation Set 51.4% 75.2% 82.1% 94.0% 95.0% 97.2% 97.7% 99.5% 99.5% 100.0% SRC Validation Set 63.3% 77.1% 89.0% 94.0% 95.9% 96.3% 100.0% 100.0% 100.0% 100.0% SRC Test Set 64.5% 77.4% 86.6% 89.4% 97.2% 97.2% 97.2% 100.0% 100.0% 100.0% AKT1 Validation Set 96.1% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% AKT1 Validation Set 79.8% 91.3% 93.3% 93.3% 97.1% 97.1% 100.0% 100.0% 100.0% 100.0% AKT1 Validation Set 80.8% 95.2% 96.2% 96.2% 97.1% 97.1% 100.0% 100.0% 100.0% 100.0% AKT1 Validation Set 89.4% 95.2% 96.2% 97.1% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% AKT1 Validation Set 88.6% 95.2% 97.1% 97.1% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% AKT1 Test Set 80.6% 94.2% 94.2% 95.1% 97.1% 97.1% 100.0% 100.0% 100.0% 100.0% PKCβ Validation Set 56.1% 84.8% 86.4% 93.9% 95.5% 97.0% 97.0% 97.0% 100.0% 100.0% PKCβ Validation Set 59.1% 83.3% 89.4% 93.9% 97.0% 97.0% 98.5% 98.5% 100.0% 100.0% PKCβ Validation Set 54.5% 83.3% 86.4% 95.5% 98.5% 98.5% 98.5% 100.0% 100.0% 100.0% PKCβ Validation Set 84.8% 90.9% 93.9% 97.0% 98.5% 98.5% 100.0% 100.0% 100.0% 100.0% PKCβ Validation Set 63.6% 81.8% 89.4% 90.9% 93.9% 97.0% 97.0% 98.5% 98.5% 100.0% PKCβ Test Set 57.6% 78.8% 86.4% 90.9% 93.9% 95.5% 98.5% 100.0% 100.0% 100.0% CDK2 Validation Set 36.1% 52.6% 70.3% 88.0% 92.5% 93.6% 99.2% 100.0% 100.0% 100.0% CDK2 Validation Set 34.4% 50.9% 71.8% 88.3% 93.8% 95.2% 100.0% 100.0% 100.0% 100.0% CDK2 Validation Set 37.2% 56.9% 75.9% 89.1% 94.9% 97.8% 98.2% 100.0% 100.0% 100.0% CDK2 Validation Set 31.3% 54.1% 69.8% 86.6% 92.9% 96.3% 100.0% 100.0% 100.0% 100.0% CDK2 Validation Set 34.8% 53.8% 73.5% 92.4% 95.1% 96.6% 98.9% 100.0% 100.0% 100.0% CDK2 Test Set 33.2% 52.3% 72.1% 88.2% 93.1% 95.4% 100.0% 100.0% 100.0% 100.0% p38α Validation Set 36.3% 57.7% 70.3% 83.0% 89.9% 93.4% 98.4% 100.0% 100.0% 100.0% p38α Validation Set 28.5% 58.2% 68.7% 80.8% 89.2% 94.1% 98.8% 100.0% 100.0% 100.0% p38α Validation Set 29.5% 57.1% 70.2% 84.5% 90.4% 95.0% 97.8% 100.0% 100.0% 100.0% p38α Validation Set 25.2% 50.9% 66.1% 77.0% 88.2% 92.5% 97.8% 100.0% 100.0% 100.0% p38α Validation Set 30.8% 59.2% 64.7% 81.0% 86.1% 90.9% 97.3% 100.0% 100.0% 100.0% p38α Test Set 24.5% 54.0% 67.5% 77.9% 86.5% 89.6% 96.3% 100.0% 100.0% 100.0% 161 APPENDICES EGFR dataset a) Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 20 AKT1 dataset c) 10 Fold improvement over random 40 60 Percentage of the dataset (deciles) 80 20 40 60 Percentage of the dataset (deciles) 80 PKC dataset d) 10 Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 100 Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 20 40 60 Percentage of the dataset (deciles) CDK2 dataset e) 10 80 100 20 40 60 Percentage of the dataset (deciles) p38a dataset f) 10 Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random Fold improvement over random Fold improvement over random Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 100 Fold improvement over random SRC dataset b) 10 Fold improvement over random Fold improvement over random 10 80 100 Validation Set Validation Set Validation Set Validation Set Validation Set Test Set Random 20 40 60 Percentage of the dataset (deciles) 80 100 20 40 60 Percentage of the dataset (deciles) 80 100 Figure S1.1 Enrichment curve of the five-fold cross validation and external validation. Each plot shows the fold improvement over random selection of active selection of actives for each decile. 162 APPENDICES Table S1.3 The top 10 Klekota-Roth fingerprint keys/substructures selected for the respective datasets. Rank AKT1 Validation Set 1st KR1 2nd KR298 3rd KR296 4th KR1192 5th KR668 6th KR3402 AKT1 Validation Set KR3640 KR3402 KR3882 KR1193 KR3750 None AKT1 Validation Set KR1501 KR2976 KR2975 KR2548 KR3402 KR3025 AKT1 Validation Set KR3402 KR3025 KR1193 KR3926 None None AKT1 Validation Set KR3402 KR3025 KR1193 KR3926 None AKT1 Test Set KR3402 KR3025 KR1193 KR3926 p38α Validation Set KR3956 None None p38α Validation Set KR3956 None p38α Validation Set KR2974 p38α Validation Set p38α Validation Set p38α Test Set 7th KR3025 8th KR3926 9th None 10th None None None None None KR1193 KR3926 None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None None KR2975 KR4080 KR4330 KR3223 KR4079 KR296 KR3882 KR4331 KR3224 KR3956 None None None None None None None None None KR3956 None None None None None None None None None KR3956 None None None None None None None None None 163 APPENDICES APPENDIX 2: EXPERIMENTAL ACTIVITY DATA Table S2.1. IC50 values of test compounds on APL (NB4) cell line. Mean IC50 (µM)[a] Reference name Linker Substituents APL cell line NB4 Cyclohexanone H 4.50 ± 0.42 Cyclohexanone 3OCH3, 4OH 2.12 ± 0.33 Cyclohexanone 3OH, 4OCH3 1.23 ± 0.33 Cyclohexanone 3OCH3, 4OCH3 4.01 ± 0.24 Cyclohexanone 3OH, 4OH 2.79 ± 0.56 Cyclohexanone 3H, 4OCH3 19.56 ± 1.53 Cyclohexanone 3OCH3, 4H 6.24 ± 0.49 Cyclohexanone 3H, 4OH 2.50 ± 0.11 Cyclohexanone 3OH, 4H 0.98 ± 0.06 10 Thiopyranone 3H, 4OCH3 17.19 ± 4.53 11 Thiopyranone 3OCH3, 4H 2.51 ± 0.28 12 Thiopyranone 3H, 4OH 2.52 ± 0.22 13 Thiopyranone 3OH, 4H 0.51 ± 0.07 14 Thiopyranone H 1.19 ± 0.23 15 Thiopyranone 3OH, 4OH 4.71 ± 0.33 16 Thiopyranone 3OCH3, 4OCH3 0.69 ± 0.02 [a] Mean of three or more independent experiments. 164 [...]... different scoring functions are combined in a variety of ways so as to achieve improvement in the prediction of docked poses and binding affinity 77-87 Despite the availability of all of these different types of scoring functions, the current state of the art of the existing scoring functions is still unable to reliably predict the native binding mode and 5 CHAPTER 1 associate free energy of binding 88 This... specifically intended for organizing, modelling and analysis of chemical entities Such tools are primarily concerned with designing novel compounds, 10 identifying the most probable lead candidates 11-14 and providing a deeper understanding of the protein-ligand interactions that are responsible for their known biological activities 15-17 2 CHAPTER 1 1.2 VIRTUAL SCREENING One of the essential aspects in CADD... virtual screening Virtual screening 18 is the computational technique that deals with the rapid identification of the compounds of interest from a large compound library The goal of virtual screening is to filter, score and rank structures of compounds using in silico methods Virtual screening may be used to select and prioritize compounds for screening in assays, 19 selecting which compounds to acquire... molecular docking, are able to provide crucial insights into the type of interactions between drug targets and the ligands 1.3 MOLECULAR DOCKING & SCORING FUNCTIONS Molecular docking is commonly used to identify potential active compounds by ranking a library of compounds based on the strength of protein-ligand interactions which are evaluated via a scoring function 38, 39 During the docking process,... prioritization of compounds in drug discovery The research work has been allocated into four parts, each catering to a different stage of the drug discovery process In the first part of the thesis, the objective was to formulate a computational workflow that can be used to prioritize compounds of interest from a primary screen hit list for reconfirmation screening, an important step in initiating lead discovery. .. different ligand orientations and conformations (collectively known as docked poses) in the binding pocket of the target macromolecule 40 Molecular docking methods allow different levels of flexibility for the protein and ligands It is commonplace for recent docking algorithms to allow complete flexibility for the ligands To a lesser extent, different levels of flexibility to side chains of the amino acid... xvi LIST OF FIGURES Figure 1.1 Stages of drug discovery and development 1 Figure 2.1 Typical workflow of compound selection and screening in the pharmaceutical industry 17 Figure 2.2 Idealised Gaussian distribution and an indication of the top X% of compounds (area under curve) 20 Figure 2.3 Idealised Gaussian distribution and an indication of n percent inhibition... 1.1 INTRODUCTION TO COMPUTATIONAL METHODS IN DRUG DISCOVERY INTRODUCTION Figure 1.1 Stages of drug discovery and development Before work is started to discover any potential new medicine for a specific disease, scientists need to investigate the underlying cause of the disease as thoroughly as possible In particular, they seek to understand how genes are altered and the related mechanism of action of. .. It will be subjected to extensive in vitro and in vivo testing to determine if it is safe enough for human testing In the next step, the candidate drug enters the development process (clinical trials) in which it will be tested in humans for its efficacy and safety Novel drug discovery and development is known to be lengthy, risky and costly It takes around 14 years 1 and up to US$1.3 billion 2 from... since it is critical for the replication of the dengue virus’ RNA In this work, a virtual screening workflow was formulated A virtual screening protocol was formulated that included docking, pharmacophoric and shape based matching techniques for the analysis of the interactions of a corporate database against the enzymatic target In the final part of the thesis, a novel application of the Taguchi Method . IN SILICO METHODOLOGIES FOR SELECTION AND PRIORITIZATION OF COMPOUNDS IN DRUG DISCOVERY YEO WEE KIANG (M.Sc. (Bioinformatics), NTU) A THESIS SUBMITTED FOR THE. thesis was to investigate the various methodologies that can be applied for the selection and prioritization of compounds in drug discovery. The research work has been allocated into four parts,. States of America). Poster title: “A Random Forest Clustering Approach to Compound Selection and Prioritization for High-Throughput Screening Campaigns”. 4. The 7th International Symposium for