Vishnu Vettrivel

Founder and CEO, Wisecube AI

Vishnu Vettrivel is the Founder and CTO of Wisecube, a startup focused on accelerating biomedical research using AI. He has decades of experience building Data platforms and teams in healthcare, financial services and digital marketing.

Past sessions

Summit 2021 Drug Repurposing using Deep Learning on Knowledge Graphs

May 26, 2021 04:25 PM PT

Discovering new drugs is a lengthy and expensive process. This means that finding new uses for existing drugs can help create new treatments in less time and with less time. The difficulty is in finding these potential new uses.

How do we find these undiscovered uses for existing drugs?

We can unify the available structured and unstructured data sets into a knowledge graph. This is done by fusing the structured data sets, and performing named entity extraction on the unstructured data sets. Once this is done, we can use deep learning techniques to predict latent relationships.

In this talk we will cover:

  • Building the knowledge graph
  • Predicting latent relationships
  • Using the latent relationships to repurpose existing drugs
In this session watch:
Alexander Thomas, Principal Data Scientist, Wisecube AI
Vishnu Vettrivel, Founder and CEO, Wisecube AI

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Summit Europe 2020 Using NLP to Explore Entity Relationships in COVID-19 Literature

November 17, 2020 04:00 PM PT

In this talk, we will cover how to extract entities from text using both rule-based and deep learning techniques. We will also cover how to use rule-based entity extraction to bootstrap a named entity recognition model. The other important aspect of this project we will cover is how to infer relationships between entities, and combine them with explicit relationships found in the source data sets. Although this talk is focused on the CORD-19 data set, the techniques covered are applicable to a wide variety of domains. This talk is for those who want to learn how to use NLP to explore relationships in text.

What you will learn
- How to extract named entities without a model
- How to bootstrap an NLP model from rule-based techniques
- How to identify relationships between entities in text.

Speakers: Alexander Thomas and Vishnu Vettrivel

Summit Europe 2019 Drug Discovery and Development Using AI

October 16, 2019 05:00 PM PT

Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore's law that it now has its own law called Eroom's Law named after it (the opposite of Moore's). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.

Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.

Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.

We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.

We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.

Summit 2019 Assessing Drug Safety Using AI

April 24, 2019 05:00 PM PT

Drug discovery and development is a long and expensive process over time has notoriously bucked Moore's law that it now has its own law called Eroom's Law named after it (the opposite of Moore). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of drug failures. Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible becomes all the more important to accelerate drug discovery and development.

This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.

Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories: 1. Classification 2. Regression 3. Read-across.

The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them. We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research.

Finally, the talk will also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.