Computer-Aided Drug Design | Pharmacometrics | Computational Chemistry/Biology | Chemical Informatics

Systems Pharmacology (PSP I)

  1. Course contact: Junmei Wang, Ph. D.
  2. Course Number: PHARM 3068
  3. Course Allowance: 3 credits
  4. Course Description: This course will teach the fundamentals of computational systems pharmacology (CSP) modeling and their applications to study drug actions and rational development of new drugs through network analysis. Theoretical concepts pertaining to computational systems pharmacology, such as drug target identification and rational drug design, will be taught. The course also includes hands-on training with the mainstream systems analysis and computer-aided drug design software, such as Simbiology of Matlab, Tetrad, Schrodinger, etc.
Pharmacometrics (PSP II)

  1. Course contact: Junmei Wang, Ph. D.
  2. Course Number: PHARM 3069
  3. Course Allowance: 3 credits
  4. Course Description: This course will provide training in the fundermentals of pharmacometrics & pharmacology modeling and their applications to study drug actions through multiscale modeling and simulations [mechanism-driven] and data analysis [data-driven]. Theoretical concepts pertaining to PSP, such as polulation pharmacokinetics/pharmacodynamics (pop PK/pD) modeling both from mechanistic and statistical points of veiw, physiologically-based pharmacokinetics (PBPK) data analysis, will be taught. The course covers the advanced pharmacokinetics/pharmacodynamics (PK/PD) topics, such as drug-drug interactions, disease progression, as well as multi-scaling PK/PD modeling of drug actions on selected diseases, including cancers and type 2 diabetes. The course also offers hands-on training on using mainstream population PK/PD and PBPK modeling and simulation software, including NONMEM and SimCYP.
Molecular Modeling

  1. Course director: Junmei Wang, Ph. D.
  2. The course is open to all graduate students and any biomedical researcher at UTSW interested in molecular modeling. No prior knowledge and experience on the field is required. Enrollment is limited to 25 students.
  3. There are two-fold objectives of this course. First of all every student learns the basic knowledge and techniques in modern molecular simulations ( MD ) and rational drug design ( CADD ), and secondly everyone knows how to start a molecular modeling study for his/her own problem during the medical research.
  4. For the first objective: introduction to the basic computer-aided drug design methods with emphases on molecular docking ( Docking Scoring ), pharmacophore modeling and virtual high throughput screening ( HTS ). Introduction to the basic molecular simulation techniques and their applications in studying the structures, dynamics and functions of biomolecular systems. The course also covers the fundamental concepts of basic electronic structure methods for both small and biomolecules.
  5. To achieve the second objective of the course, main stream software packages for each topic will be briefly introduced and guided exercises of using representative software packages will be held in the classroom. Students are expected to work on real projects using the selected software packages (most of them are in the public domain) as homework to further deepen their understanding on the taught materials.
  6. This course consists of a combination of classroom lectures, guided exercises and homework assignments. Total lecture time and guided exercises will be 10 hours.
Computational Chemistry

Computational chemistry is a multi-disciplinary field that has been growing rapidly during the last few years. This course will cover computer models and experimental aspects of molecules and their behavior in gas and condensed phases; quantum and molecular mechanics; geometry optimization methods; conformational analysis; molecular dynamics and Monte Carlo simulations; and some challenging topics in computational chemistry including free energy calculations, solvation (both explicit and implicit models), entropy calculations, protein modeling, etc. I will also briefly discuss some topics on molecular modeling and computer-aided molecular design (CAMD) including de novo design techniques; docking and scoring; quantitative structure-activity relationships (QSAR); comparative molecular field analysis (CoMFA); pharmacophore modeling; molecular diversity and combinatorial libraries; molecular similarity; drug-likeness analysis and practical aspects of molecular modeling - computable quantities, cost and efficiency, hardware and software, etc. However, most topics in molecular modeling/CAMD can be moved to a chemical informatics course if available.

Chemical Informatics

Chemical Informatics is a newly-emerged informatics field (compared to bioinformatics), chemical informatics is growing rapidly and becoming a core part of modern drug discovery. Its interdisciplinary nature keeps pushing the boundaries of chemistry, computer science, statistics, visualization methods and new scientific technique. Chemical Informatics covers a wide variety of applications and specialties, particularly in the pharmaceutical industry, where the rapid increase in new technologies in drug discovery puts chemical informatics at the forefront of drug design. This course will cover the most exciting chemical informatics topics that include 2D and 3D representation of chemical structures; QSAR and QSPR (especially in silico ADMET prediction), virtual screening with molecular docking, fingerprint comparison and pharmacophore mapping; data analysis (especially for high throughput screening) with a variety of statistical approaches; compound library design; chemical and biological structure visualization; computer languages and programming toolkits (such as CDK and OECHEM of Openeye); integration with other –ics (bioinformatics, genomics, systems biology) and laboratory, etc.


Bioinformatics is the application of statistics and computer to the field of molecular biology. Unlike chemical informatics, it is a well-defined discipline and there are numerous algorithms and software packages developed to analyze biological information. This course will not only introduce the traditional activities in bioinformatics which include sequence alignment and similarity search, computational evolution phylogenetic analysis and microarray data analysis, but also cover the basic topics of handling biological three dimensional structural data, such as structural alignment and structure prediction, molecular docking and drug design, etc. Moreover, the basic program skills using perl, scripts and web-based programming will be discussed.