S. So

发表

Structure-activity relationships from molecular similarity matrices.

A. Good, S. So, W. Richards, 1993, Journal of medicinal chemistry.

Genetic neural networks for quantitative structure-activity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors.

M. Karplus, S. So, Martin Karplus, 1996, Journal of medicinal chemistry.

Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors.

S. So, W. Richards, 1992, Journal of medicinal chemistry.

Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks.

M Karplus, M. Karplus, S. So, 1996, Journal of medicinal chemistry.

Enumeration and Visualization of Large Combinatorial Chemical Libraries

S. So, 2014 .

Potent MCH-1 receptor antagonists from cis-1,4-diaminocyclohexane-derived indane analogs.

S. So, Y. Qian, Yonglin Ren, 2013, Bioorganic & medicinal chemistry letters.

In vivo emergence of a novel mutant L159F/L320F in the NS5B polymerase confers low-level resistance to the HCV polymerase inhibitors mericitabine and sofosbuvir.

S. So, X. Tong, Lewyn Li, 2014, The Journal of infectious diseases.

Development of Neural Network QSPR Models for Hansch Substituent Constants. 1. Method and Validations

Sung-Sau So, Ting-Lan Chiu, S. So, 2004, J. Chem. Inf. Model..

Discovery of 1-arylcarbonyl-6,7-dimethoxyisoquinoline derivatives as glutamine fructose-6-phosphate amidotransferase (GFAT) inhibitors.

S. So, Shaoqing Chen, Nam T Le, 2011, Bioorganic & medicinal chemistry letters.

Development of a robust crystallization platform for immune receptor TREM2 using a crystallization chaperone strategy.

S. So, H. Su, A. Brooun, 2020, Protein expression and purification.

Discovery of camphor-derived pyrazolones as 11β-hydroxysteroid dehydrogenase type 1 inhibitors.

S. So, Yonglin Ren, R. Taub, 2014, Bioorganic & medicinal chemistry letters.

Discovery of 2-[3,5-dichloro-4-(5-isopropyl-6-oxo-1,6-dihydropyridazin-3-yloxy)phenyl]-3,5-dioxo-2,3,4,5-tetrahydro[1,2,4]triazine-6-carbonitrile (MGL-3196), a Highly Selective Thyroid Hormone Receptor β agonist in clinical trials for the treatment of dyslipidemia.

S. So, I. Klein, Y. Qian, 2014, Journal of medicinal chemistry.

Discovery of pyrazolo[3,4-d]pyrimidine derivatives as GPR119 agonists.

S. So, M. Myers, G. Saha, 2014, Bioorganic & medicinal chemistry letters.

A potent seven-membered cyclic BTK (Bruton's tyrosine Kinase) chiral inhibitor conceived by structure-based drug design to lock its bioactive conformation.

S. So, C. Brotherton-Pleiss, F. Lopez-Tapia, 2019, Bioorganic & medicinal chemistry letters.

Discovery of [4-Amino-2-(1-methanesulfonylpiperidin-4-ylamino)pyrimidin-5-yl](2,3-difluoro-6- methoxyphenyl)methanone (R547), a potent and selective cyclin-dependent kinase inhibitor with significant in vivo antitumor activity.

Nader Fotouhi, Sung-Sau So, S. So, 2006, Journal of medicinal chemistry.

Design and synthesis of selective, dual fatty acid binding protein 4 and 5 inhibitors.

S. So, B. Kuhn, S. Ceccarelli, 2016, Bioorganic & medicinal chemistry letters.

Overview of Artificial Neural Networks

Yi Han, Sung-Sau So, Jinming Zou, 2009, Artificial Neural Networks.

Discovery of Potent and Orally Active p53-MDM2 Inhibitors RO5353 and RO2468 for Potential Clinical Development.

S. So, L. Vassilev, B. Graves, 2014, ACS medicinal chemistry letters.

Discovery of non-Boronic acid Arginase 1 Inhibitors through Virtual Screening and Biophysical Methods.

S. So, X. Chai, D. McLaren, 2023, Bioorganic & medicinal chemistry letters.

A simple statistical test to infer the causality of target/phenotype correlation from small molecule phenotypic screens

Yang He, Xin Wei, David Mark, 2012, Bioinform..

Quantitative Structure‐Activity Relationships

S. So, 2000 .

A comparative study of ligand-receptor complex binding affinity prediction methods based on glycogen phosphorylase inhibitors

Sung-Sau So, Martin Karplus, M. Karplus, 1999, J. Comput. Aided Mol. Des..

Fragment library design: using cheminformatics and expert chemists to fill gaps in existing fragment libraries.

Christian Fischer, Peter S Kutchukian, Chris L Waller, 2015, Methods in molecular biology.

Development of Neural Network QSPR Models for Hansch Substituent Constants. 2. Applications in QSAR Studies of HIV-1 Reverse Transcriptase and Dihydrofolate Reductase Inhibitors

Sung-Sau So, Ting-Lan Chiu, S. So, 2004, J. Chem. Inf. Model..

Design of Libraries Targeting Protein–Protein Interfaces

Harald Mauser, Martin Stahl, Binh Vu, 2013, ChemMedChem.

Quantitative Structure-Activity Relationship Studies of Progesterone Receptor Binding Steroids

Steven P. van Helden, Vincent J. van Geerestein, Sung-Sau So, 2000, J. Chem. Inf. Comput. Sci..

Use of quantitative structure‐property relationships to predict the folding ability of model proteins

M. Karplus, S. So, A. Dinner, 1998, Proteins.

Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations.

M Karplus, M. Karplus, S. So, 1997, Journal of medicinal chemistry.

Development of Quantitative Structure-Property Relationship Models for Early ADME Evaluation in Drug Discovery. 1. Aqueous Solubility

Ruifeng Liu, Sung-Sau So, S. So, 2001, J. Chem. Inf. Comput. Sci..

Development of Quantitative Structure-Property Relationship Models for Early ADME Evaluation in Drug Discovery. 2. Blood-Brain Barrier Penetration

Ruifeng Liu, Hongmao Sun, Sung-Sau So, 2001, J. Chem. Inf. Comput. Sci..

Statistical Analysis of Protein Folding Kinetics

M. Karplus, S. So, A. Dinner, 2002 .

STATISTICAL ANALYSIS OF PROTEIN FOLDING KINETICS

M. Karplus, S. So, A. U.S., 2002 .

A Crowd‐Based Process and Tool for HTS Hit Triage

Paul Gillespie, Zhengwei Peng, Arjun Narayanan, 2013, Molecular informatics.

Automated Pharmacophore Query Optimization with Genetic AlgorithmsA Case Study Using the MC4R System

Lei Jia, Hongmao Sun, Sung-Sau So, 2007, J. Chem. Inf. Model..

Enhancing Specificity and Sensitivity of Pharmacophore-Based Virtual Screening by Incorporating Chemical and Shape Features-A Case Study of HIV Protease Inhibitors

Hongmao Sun, Sung-Sau So, Deepangi Pandit, 2006, J. Chem. Inf. Model..

Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 2. Applications.

M Karplus, M. Karplus, S. So, 1997, Journal of medicinal chemistry.

A Crowd‐Based Process and Tool for HTS Hit Triage

S. So, W. V. So, M. Weisel, 2013, Molecular informatics.

Application of Neural Networks: Quantitative Structure-Activity Relationships of the Derivatives of 2,4-Diamino-5-(substituted-benzyl) pyrimidines as DHFR Inhibitors.

W. G. Richards, S. So, W. Richards, 1993 .

Bioinformatics Discovery Note Systems Biology a Simple Statistical Test to Infer the Causality of Target/phenotype Correlation from Small Molecule Phenotypic Screens

S. So, Qing Xiang, Xin Wei, 2022 .