Drug Discovery


New drug development can be divided into two phases: (1) discovery, which tends to be chemistry-centric, and (2) translation, which focuses on animal and clinical therapeutic responses. The figure to the right illustrates how few new drugs make the journey from initial drug lead discovery to final approval in humans as a “Drug Discovery Funnel.” During the first discovery phase, only 4% of initial drug leads progress to early animal work. SMDA’s Drug Discovery working group aims to increase success rates in early drug screening and optimization by leveraging large-scale drug datasets and modifying in vitro validation assays to improve clinical validity.

In the second translational phase, a mere 0.2% of drug leads are approved for use in humans. SMDA’s Translational working group aims to increase success rates in clinical translation through modeling drug response (PK-PD) and better matching drug leads to the patient populations who will benefit from them (diagnostics).

Overall, these two working groups strive to fulfill a major strategic goal of SMDA and POH: to better integrate research across the drug development process to increase success rates and reduce drug development costs. This page provides an overview of SMDA’s Drug Discovery working group’s current priority areas and shared resources.

 

Artificial Intelligence in Drug-Discovery: Drug-centric Datasets

In drug discovery and development, five major drug-centric data types are driving efforts to leverage artificial intelligence for improved drug discovery. The first data type is the Chemical Properties of Drugs and drug-like molecules. Currently, approximately 10,000 drugs are used clinically in humans, and around 100 million small molecules can be evaluated computationally for their chemical similarity to drug-like molecules. Clinical endpoints that best correlate with drug properties include absorption (e.g., intestines), distribution to different body compartments, metabolism by liver enzymes, and excretion via feces and urine. Chemical properties can also be major determinants of toxicity; for example, aromatic and electrophilic molecules can have genotoxic effects on cells.

The second major type of drug data is Drug Targets Affinities. Approximately 2,000 “druggable” genes exist within the human genome of about 20,000 protein-coding genes. Only around 10% of the human genome is considered druggable because traditional small molecule drugs require a small “binding pocket” to fit via a “lock and key” mechanism. Typical drug-target data types include relative binding affinities from both virtual and experimental screening methods with purified proteins.

Cellular signaling pathways are another critical dataset for understanding drug response in vitro, as they bridge the physical binding of a drug to its target with the downstream therapeutic response (e.g., cell killing for bacteria or cancer). Cellular Drug-response data is the most complex form of drug data available before animal work. Typically, the response of bacterial or cancer cells to around 10,000 drugs can be evaluated using robotic technology that automates traditional drug-response assays conducted in the laboratory.

 


Tactical Priorities

  • Recruit Workforce:  with interests in medicinal chemistry and chemo-informatics
  • Train Workforce:  to use emerging tools of
    • chemoinformatics
    • data-science
    • artificial intelligence
  • Build Collaborations:  between relevant disciplines
    • computational chemists
    • medicinal chemists
    • bioinformaticians
    • pharmacologists
  • Automate:   laborious and iterative processes to:
    • increase efficiency of research
    • increase access to advanced computational methods
  • Map Community Datasets:
    • chemoinformatics
    • virtual screening
    • drug-target affinity (e.g. kinome-scan)
    • high throughput screening
    • clinical drug data

Strategic Priorities

  • Accessible Chemo-informatics: to facilitate
    • Quantitative Structure Activity Relationship (QSAR) studies
    • Molecular Probe Design (e.g. fluorophores, activity probes)
  • Accessible Virtual Screening: to facilitate
    • ab initio drug design
    • natural ligand discovery
    • drug-lead target identification
  • Accessible High Throughput Screening: to facilitate
    • drug-lead discovery for specific disease models
    • structural activity relationship (SAR) studies for specific drug-leads
    • disease model studies for pre-existing SAR-libraries at UGA
  • Accessible Pharmacokinetic-Toxicity Analysis:
    • computational methods
    • in vitro methods
    • in vivo methods

Current Members

  • Eugene Douglass
  • Uma Singh
  • Robert Huigens
  • Jonathan Mochel
  • Karin Allenspach
  • Natarajan Kannan
  • Steve Maher
  • Anthony Roberto

Software Tools


Cleaned Datasets


Key Performance Indicators: summary statistics

 

KPI Category KPI Current Value
Research and Publications Number of Published Papers
Impact Factor of Journals
Citations
Conference Presentations
Collaboration and Engagement Interdisciplinary Projects
External Collaborations
Collaborative Publications %
Workshops and Seminars
Funding and Grants Research Grants Received $
Grant Applications Submitted
Grant Success Rate %
Data and Tools Datasets Published
Software Tools Developed
Tool Adoption downloads
Training and Development Students Supervised
Training Programs
Skill Development certificates
Impact and Outreach Societal Impact
Media Mentions
Public Engagement events
Operational Efficiency Project Completion Rate
Data Management Practices % compliance
Resource Utilization % efficiency
Innovation and Excellence Awards and Recognitions
Innovative Solutions breakthroughs
Feedback and Improvement Stakeholder Feedback satisfaction
Continuous Improvement # iterations

 

 

 


Key Performance Indicators: specific items list

 


Protected Content

  • Research and Publications
    • Number of Published Papers
    • Impact Factor of Journals
    • Citations
    • Conference Presentations
  • Collaboration and Engagement
    • Interdisciplinary Projects
    • External Collaborations
    • Collaborative Publications
    • Workshops and Seminars
  • Funding and Grants
    • Research Grants Received
    • Grant Applications Submitted
    • Grant Success Rate
  • Data and Tools
    • Datasets Published
    • Software Tools Developed
    • Tool Adoption
  • Training and Development
    • Students Supervised
    • Training Programs
    • Skill Development
  • Impact and Outreach
    • Societal Impact
    • Media Mentions
    • Public Engagement
  • Operational Efficiency
    • Project Completion Rate
    • Data Management Practices
    • Resource Utilization
  • Innovation and Excellence
    • Awards and Recognitions
    • Innovative Solutions
  • Feedback and Improvement
    • Stakeholder Feedback
    • Continuous Improvement