This course on artificial intelligence for drug discovery teaches how to apply deep and reinforcement learning techniques in small-molecule drug discovery. You will also gain foundational skills, including understanding deep learning algorithms' theoretical background and working principles. You’ll navigate several deep learning frameworks, learn the intricacies of neural networks, and master the theories and implementation of reinforcement learning. The goal is to equip you with the knowledge and expertise to advance small-molecule drug discovery through artificial intelligence. Ultimately, you’ll be completing 8 mid-course capstone projects, 1 final capstone project, and 6 quizzes which we anticipate will solidify your expertise in this critical domain.
AI for Drug Discovery: Deep and Reinforcement Learning
COHORT-BASED COURSE
Current cohort in progress.
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NEXT COHORT
Jun 1-Aug 31, 2025
DURATION
3 Months
LEVEL
Intermediate
HOSTED BY
Pankaj Mishra, PhD
Industrial Molecular AI Builder, Co-founder and CTO at Future Therapeutics, Co-founder of Neovarsity
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About the Course
Flexible learning options
- Attend live (virtual) lectures
- Access recorded lectures in your private dashboard
Practical application
- Apply your skills through hands-on projects
- Engage in real-world case studies
Personalized learning experiences
- Tailored support and guidance
- 24x7 support by our dedicated support team
Specialized community access
- To our Members-only Slack community
- To our invite-only deep tech global Slack community
Syllabus Overview
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Course IntroductionReading
Overview of Course CurriculumReading
Significance of AI in drug discoveryReading
Course FAQs
Meet your Instructors
Pankaj Mishra, PhD
Instructor
Industrial Molecular AI Builder, Co-founder and CTO at Future Therapeutics, Co-founder of Neovarsity
I’m the Co-founder and CTO of Future Therapeutics, an AI-native biotech based in Berlin, where we build proprietary AI systems for drug discovery. I hold a PhD from the University of Freiburg, specializing in small molecule AI, and I’m trained in building models for low-data simulation and modeling, the reality most discovery teams operate in. I’ve been doing this long before it became mainstream, back in 2018 I was already building deep learning systems to explore “ultra-large chemical space”. Over the past few years, my focus has been generative molecular design. I’m also a Co-founder of Neovarsity, and since 2021 I’ve taught scientists and engineers across biopharma how to apply AI in real R&D workflows, including teams from J&J, Bayer, Takeda, Novartis, and others.
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AI for Drug Discovery: Deep and Reinforcement Learning
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01 Jun, 2025
This course is tailored for individuals actively involved in small-molecule drug discovery, including researchers, pharmaceutical professionals, medicinal chemists, machine learning engineers, data scientists, computational chemists, and cheminformaticians. The content is designed to empower participants with the skills needed to effectively apply artificial intelligence in the pursuit of accelerating and optimizing the small-molecules drug discovery process.
No, a background in artificial intelligence is not required for enrollment. This course covers foundational skills, including the theoretical background of the algorithms and their working principles. It is designed to provide a comprehensive understanding for individuals at various levels of expertise, making it accessible to beginners in the field of artificial intelligence for drug discovery.
Yes, a strong background in cheminformatics and machine learning for small-molecule drug discovery is crucial for this course. Given the complexity of the field and the necessity to handle chemical and biological data correctly, it is essential to have prior knowledge in these areas. Additionally, understanding how to identify and manage data biases in chemical data is of utmost importance, and these critical topics are covered in the Advanced Machine Learning for Drug Discovery course. We highly recommend completing that course before enrolling in this curriculum to ensure a comprehensive foundation.
In this course, you will gain comprehensive knowledge and practical skills in applying artificial intelligence, deep learning, and reinforcement learning to the field of small-molecule drug discovery. Specific topics include molecular data representation, neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), reinforcement learning techniques, and their applications in drug discovery. Additionally, you will engage in hands-on projects and quizzes, allowing you to implement learned concepts in real-world scenarios.
Yes, absolutely! We encourage participants to explore reimbursement options from their company or university. Many organizations have a Learning and Development (L&D) budget set aside for employee training and upskilling initiatives. We recommend reaching out to your manager or supervisor to inquire about the availability of such a budget and the process for reimbursement. If you have any questions or need assistance in navigating this process, feel free to reach out to us at suppor@]neovarsity.org or start an online chat. We're available 24x7 to help you.
The live sessions will take place every Saturday and Sunday at 19:00 CEST (8 PM German time), starting on August 2. Each session will run for 2 hours.
If you are unable to attend a live session for any reason, you will have access to the recording through your Neovarsity account.
Yes, the course includes 8 mid-course capstone projects, 1 final capstone project, and 6 quizzes that allow you to apply the concepts learned in real-world scenarios, enhancing your understanding and skills.
Yes, upon completing the course and passing all quizzes and submitting all capstone projects, you will receive a certificate of completion.