Making your next compound-related decision

One of the main questions to answer during the drug design process is: “Which compound should we make next?” You have to be efficient with both time and resources to come up with a prioritized set of compounds, because the available chemical space is huge and your chemists can only explore small amounts of it. It is becoming increasingly difficult to navigate through this space, prompting a growing need for reliable data to inform the decisions about how to move forward with a particular set of compounds. But how should we do it?

The ideal decision-making process

In an ideal scenario, compound-related decisions are made objectively, excluding any ambiguous factors. One effective tool that leads us towards this goal is the utilization of precise and reliable predictive data. Establishing a scoring system based on predictive data, which can measure various parameters and rank molecules accordingly, is a key step given the breadth and depth of available data. Balancing these scoring functions ensures nuanced consideration of each parameter.
The scoring system based property profile as a decision making methodology is a strong candidate when striving for objectivity. Its goal is to create a comprehensive understanding of each compound by considering multiple physicochemical parameters. These parameters have distinct ranges, representing both highly favorable and less optimal areas. Summarizing the properties of a molecule into a single numerical value like this, enables objective ranking.

In ambiguous cases, project teams should rely on collaborative decision-making, which can be challenging, especially for distributed teams. In such cases, they can use various helper tools, like voting or commenting to better support the consideration of diverse opinions. A consensus can be reached that considers everyone's input, improving the overall objectivity of the process. 

Challenges in decision-making

Data fragmentation

Leveraging collective decision-making methods has its own unique challenges. One particular problem is data fragmentation, caused by the complex and geographically scattered  nature of drug development, involving multiple stakeholders and applications. Lack of integration and standardization often leads to unharmonized data isolated and scattered across multiple sources: slides, databases, files and email, often in incompatible formats.
Additionally, research institutions and Contract Research Organizations (CROs) often maintain separate data repositories, leading to isolated data silos, which inhibit efficient data sharing and collaboration and slow down the research process. The inability of static legacy data management systems to be tightly integrated with other tools further aggravates data fragmentation and data quality in a drug discovery project.

Hindered interactivity

People are most effective at processing data when it is displayed visually. Regular team meetings have long been supported by PowerPoint presentations which, although can convey the immediate message, do not offer interactive support. Drug discovery requires collaboration between experts from diverse fields such as chemistry, biology, pharmacology and informatics. Each discipline may have its own specialized tools and databases, making it challenging to share data effectively. Sharing spreadsheets and slide decks among team members or different groups often generates redundant files and leads to the notorious "versioning" problem. 

Make your next compound-related decision with Design Hub

Struggling with scattered data and inefficient collaboration in your DMTA projects? With Design Hub you can seamlessly integrate and share data, improve interactivity, and streamline your collaborative efforts for more efficient compound-related decisions. Explore our Showcase document to see how Design Hub can aid you in your drug discovery projects.