View source for Salesforce, inc. (20240203532). SYSTEMS AND METHODS FOR LANGUAGE MODELING OF PROTEIN ENGINEERING simplified abstract

=SYSTEMS AND METHODS FOR LANGUAGE MODELING OF PROTEIN ENGINEERING=


==Organization Name==

[[:Category:salesforce, inc.|salesforce, inc.]]


[[Category:salesforce, inc.]]

==Inventor(s)==

[[:Category:Ali Madani of San Francisco CA (US)|Ali Madani of San Francisco CA (US)]][[Category:Ali Madani of San Francisco CA (US)]]

[[:Category:Bryan Mccann of Menlo Park CA (US)|Bryan Mccann of Menlo Park CA (US)]][[Category:Bryan Mccann of Menlo Park CA (US)]]

[[:Category:Nikhil Naik of Mountain View CA (US)|Nikhil Naik of Mountain View CA (US)]][[Category:Nikhil Naik of Mountain View CA (US)]]

==SYSTEMS AND METHODS FOR LANGUAGE MODELING OF PROTEIN ENGINEERING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240203532 titled 'SYSTEMS AND METHODS FOR LANGUAGE MODELING OF PROTEIN ENGINEERING

The present disclosure introduces systems and methods for controllable protein generation, utilizing neural network models developed for natural language processing (NLP) and transformer architectures for generative modeling in protein engineering.

* Leveraging neural network models and techniques from NLP for protein generation
* Using transformer architectures for language modeling in generative modeling for protein engineering
* Applying advanced AI techniques to protein engineering for enhanced control and precision
* Enhancing protein generation processes through innovative machine learning approaches
* Improving efficiency and accuracy in protein engineering through AI-driven methods

Potential Applications:
- Drug discovery and development
- Biomedical research
- Protein design and optimization
- Bioinformatics and computational biology
- Precision medicine

Problems Solved:
- Enhancing control and precision in protein generation
- Improving efficiency and accuracy in protein engineering
- Accelerating drug discovery processes
- Facilitating protein design and optimization
- Advancing research in bioinformatics and computational biology

Benefits:
- Increased control and precision in protein engineering
- Accelerated drug discovery and development
- Enhanced efficiency and accuracy in protein generation
- Facilitated protein design and optimization
- Improved research capabilities in bioinformatics and computational biology

Commercial Applications:
Title: AI-driven Protein Generation for Enhanced Drug Discovery
This technology can be utilized in pharmaceutical companies, research institutions, and biotech firms for accelerating drug discovery processes, optimizing protein design, and advancing biomedical research. The market implications include improved efficiency, reduced costs, and enhanced competitiveness in the biotechnology and pharmaceutical industries.

Questions about AI-driven Protein Generation for Enhanced Drug Discovery:
1. How does this technology impact the field of drug discovery?
- This technology accelerates drug discovery processes by enhancing control and precision in protein engineering, leading to faster development of new drugs.
2. What are the potential applications of AI-driven protein generation in biomedical research?
- AI-driven protein generation can be applied in various areas of biomedical research, such as drug development, personalized medicine, and disease modeling.


==Original Abstract Submitted==

the present disclosure provides systems and methods for controllable protein generation. according to some embodiments, the systems and methods leverage neural network models and techniques that have been developed for other fields, in particular, natural language processing (nlp). in some embodiments, the systems and methods use or employ models implemented with transformer architectures developed for language modeling and apply the same to generative modeling for protein engineering.



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