3. Instructions for a Life in Science

In episode 3, Instructions for a Life in Science, we talk to Dr. Vanita Sood about her experiences as a drug hunter. Vanita candidly shares her career journey, reflecting back on the choices she made, the opportunities and challenges she navigated, and the transformational science she drove.  She describes her recent transition from a large pharma company to a startup environment and the critical considerations she’s had to deal with. Throughout we talk about AI in discovery, the science of serendipity, and Mary Oliver the naturist and poet. Vanita expounds the spirit of paying attention, being astonished, and sharing this enthusiasm with others.. Strap in folks. Takeaways:

• Dr. Vanita Sood has 15 years of experience in drug discovery.
• AI in drug discovery has shifted from skepticism to enthusiasm.
• Switching fields can provide new perspectives and opportunities.
• Drug discovery requires teamwork and collaboration.
• Building connections in academia can lead to industry opportunities.
• The patient-centric approach drives drug discovery priorities.
• Data generation for AI needs to be consistent and relevant.
• Human insight is essential for interpreting complex biological data.
• Exploring new chemical spaces is crucial for innovation.
• Serendipity can lead to unexpected breakthroughs in research. Relying on machine learning for drug discovery raises concerns about data complexity.
• The best drug discoveries often come from unexpected patterns in data.
• Documentation in pharma is generally better than in academia, but still has gaps.
• Machine learning models need interpretability to ensure reliability and understanding.
• Transitioning from a large pharma environment to a startup presents unique learning opportunities.
• Collaboration in the pharma industry fosters a collegial environment despite competitive pressures.
• Terminated projects can provide valuable data for machine learning models.
• Attention to detail is crucial in scientific discovery and data curation.
• Balancing deep expertise with a general understanding of other disciplines is essential in drug discovery.
• AI and machine learning are transforming the landscape of drug discovery, requiring new skill sets.