AI for Drug Discovery: Deep and Reinforcement Learning

Name: AI for Drug Discovery: Deep and Reinforcement Learning
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COHORT-BASED COURSE

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AI for Drug Discovery: Deep and Reinforcement Learning

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Sun, 2nd February, 2025

COURSE DURATION

3 Months

COURSE LEVEL

Intermediate

€449.00 one-time payment

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Feb 2-May 4, 2025

3 Months

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HOSTED BY

Pankaj Mishra, PhD

Molecular AI Specialist with 13+ Years of Experience | Founder & CTO, Future Therapeutics | Building Proprietary AI Infrastructure for Drug Discovery | Co-founder, Neovarsity

Pre-requisites

You should be familiar with:

Machine Learning for Drug Discovery

About the Course

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, y

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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

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Tailored support and guidance
24x7 support by our dedicated support team

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Syllabus Overview

Course Introduction
Overview of Course Curriculum
Significance of AI in drug discovery
Course FAQs

Meet your Instructors

Pankaj Mishra, PhD

Instructor

Molecular AI Specialist with 13+ Years of Experience

Dr. Pankaj Mishra is the co-founder of Neovarsity, a Berlin-based deep-tech learning venture. He is also the founder of Future Therapeutics, a Berlin-based AI-native therapeutics discovery company that builds and leverages proprietary AI infrastructure to find new cures. He specializes in molecular artificial intelligence and holds a Ph.D. from the University of Freiburg, Germany. He earned his master's degree in pharmaceutical chemistry from the renowned Indian Institute of Technology (BHU), India. During his academic tenure, he co-authored research publications in esteemed journals, including the Journal of Medicinal Chemistry, European Journal of Medicinal Chemistry, Nature Immunology, and Nature Cell Biology.

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AI for Drug Discovery: Deep and Reinforcement Learning

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02 Feb, 2025

One-Time Payment

€449.00

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One year complete access
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Frequently Asked Questions

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 course is designed to be completed within 12-14 weeks. We recommend dedicating at least 6 hours per week to fully benefit from the course content and to ensure adequate practice.

You will have access to the course for 1 year from the date of your registration. Instructor support is available for 3 months from the day of your registration.

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.