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Abstract:
We'll discuss how using neural networks and deep reinforcement learning can be used to design potential drug candidates. The pharmaceutical industry is crying out for a revolution in thinking and practice; the traditional methods of drug discovery and development are no longer working well. To continue to prosper, either R&D costs must be lowered or the rate of discovery for the new drugs must be drastically increased. We'll talk about how AI offers an opportunity to transform the field and dramatically accelerate the design of new drug candidates. The unique proposition of AI is the ability to learn directly from past experience and capture hidden dependences from both structured and unstructured data. As the chemical data is getting bigger, deep learning methods coupled with fast GPU computations make it possible to process vast amounts of information to find clinically relevant relationships and overcome drug discovery bottlenecks.
We'll discuss how using neural networks and deep reinforcement learning can be used to design potential drug candidates. The pharmaceutical industry is crying out for a revolution in thinking and practice; the traditional methods of drug discovery and development are no longer working well. To continue to prosper, either R&D costs must be lowered or the rate of discovery for the new drugs must be drastically increased. We'll talk about how AI offers an opportunity to transform the field and dramatically accelerate the design of new drug candidates. The unique proposition of AI is the ability to learn directly from past experience and capture hidden dependences from both structured and unstructured data. As the chemical data is getting bigger, deep learning methods coupled with fast GPU computations make it possible to process vast amounts of information to find clinically relevant relationships and overcome drug discovery bottlenecks.  Back
 
Topics:
AI and DL Research, Computational Biology and Chemistry, AI in Healthcare
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9110
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