Periodic Reporting for period 1 - in silico bio-evolutio (in silico bio-evolutio - novel AI paradigm for molecular biology)
Berichtszeitraum: 2023-01-01 bis 2023-12-31
The main goal of the project is to help in the area of human health and the keynote behind it is: Life Is Overriding Value.
In practice, this goal can be achieved by accelerating activities in laboratory conditions in vitro / in vivo and the directional process of scientific thought created around this vector. Our mission is to expand access to in-silico methodologies that address both of the above goals. At the same time, scientific thought is developing in the heads of individual scientists and their groups - this process is still not fast enough and could be significantly accelerated by stimulating collaboration between individual scientists and groups, and as a result the dynamic creation of both: new ad hoc and long-term research groups. This is the vision that guides us in the implementation of the project and which you have a chance to deliver both: a number of outcomes for various groups of patients, but also the scientific community in terms of the pace of development of molecular therapies and, at the same time, the integration of their efforts. It is also a way to create the effect of strengthening the natural intelligence of research groups through AI tools, which in such use would not replace laboratory staff but make it more effective. This is a vision for sustainable co-evolution of technology with human capabilities, which will not be an addition to modern technologies, but will remain superior to this technology, at the same more friendly towards other scientists as co-contributors to distributed and shared intelligence, which is the future of humanity.
Camino Science is a cutting-edge biotechnology company that specializes in leveraging the power of artificial intelligence (AI) models to predict interactions between organisms. Our mission is to revolutionize the field of biotechnology by harnessing the vast potential of AI-driven systems for a deeper understanding of the complex relationships and interactions that shape the biological world.
In practice, this goal can be achieved by accelerating activities in laboratory conditions in vitro / in vivo and the directional process of scientific thought created around this vector. Our mission is to expand access to in-silico methodologies that address both of the above goals. At the same time, scientific thought is developing in the heads of individual scientists and their groups - this process is still not fast enough and could be significantly accelerated by stimulating collaboration between individual scientists and groups, and as a result the dynamic creation of both: new ad hoc and long-term research groups. This is the vision that guides us in the implementation of the project and which you have a chance to deliver both: a number of outcomes for various groups of patients, but also the scientific community in terms of the pace of development of molecular therapies and, at the same time, the integration of their efforts. It is also a way to create the effect of strengthening the natural intelligence of research groups through AI tools, which in such use would not replace laboratory staff but make it more effective. This is a vision for sustainable co-evolution of technology with human capabilities, which will not be an addition to modern technologies, but will remain superior to this technology, at the same more friendly towards other scientists as co-contributors to distributed and shared intelligence, which is the future of humanity.
Camino Science is a cutting-edge biotechnology company that specializes in leveraging the power of artificial intelligence (AI) models to predict interactions between organisms. Our mission is to revolutionize the field of biotechnology by harnessing the vast potential of AI-driven systems for a deeper understanding of the complex relationships and interactions that shape the biological world.
Our company has undertaken a number of intensive activities in the area of developing the technology behind the in-silico bio-evolutio platform. First of all, in terms of the results of the substantive part responsible for the properties of the prediction engine, very good qualitative and quantitative results were achieved for individual layers and their AI / ML models aimed at addressing molecular problems defined, including for microbial data. It mainly concerns the prediction of phenotypes corresponding to specific molecular questions asked by the user and the generation and verbalization of complex insights behind the given predictions. At current stage models accuracy has been improved between 2% - 5% compared to previous versions.
At the same time, the generic methodology of building AI / ML models allowed us to scale both R&D processes and their production use in a mass application.
The developed mathematical and architectural solutions of AI / ML gave us the IP material that is the basis of international patent applications. At the same time, the existing solutions have aroused interest in the world of molecular biology science and allow for both scientific and commercial partnerships and the definition of a number of biotechnological usecases in emerging markets.
At the same time, the generic methodology of building AI / ML models allowed us to scale both R&D processes and their production use in a mass application.
The developed mathematical and architectural solutions of AI / ML gave us the IP material that is the basis of international patent applications. At the same time, the existing solutions have aroused interest in the world of molecular biology science and allow for both scientific and commercial partnerships and the definition of a number of biotechnological usecases in emerging markets.
The achieved qualitative and quantitative results of the platform allow us to address new groups of users in the in silico area for applications in the molecular biology markets. Both the quality of prediction and innovation in the sense of navigating the identified insights gives a number of applications and has the potential to significantly reduce the costs of in silico experimentation and intellectual work related to the interpretation of these results. At the same time, it gives the potential for group cooperation of scientists and overcoming barriers related to the confidentiality of their work, or rather allows for the creation of natural synergies between them. This aspect, together with the quality of the platform's results, can undoubtedly be an important element in the acceleration of scientific processes conducted on molecular data. As a consequence, it also means much more stable and faster results of experiments, leading to ready-made candidates for molecular solutions going further to the stages of clinical trials and being the basis for specific molecular therapies. It is also a guarantee for a faster and effective response to possible pandemics in the future.
Main needs:
-Further in vitro studies confirming the predictive results of the AI engine.
-Ensuring broadband and international protection in the IP area.
-Demonstrating the achieved results to both the scientific and commercial environment (through publications and speeches in defined channels of reaching out, including scientific conferences and trade fairs).
-Access to our sources of financing both the investment world and public funds allowing for scalable access to international markets of selected biotechnology sectors.
-Cooperation with defined groups working on legislation related to emerging markets, including in particular the use of in-silico methods and standardization of their use.
Main needs:
-Further in vitro studies confirming the predictive results of the AI engine.
-Ensuring broadband and international protection in the IP area.
-Demonstrating the achieved results to both the scientific and commercial environment (through publications and speeches in defined channels of reaching out, including scientific conferences and trade fairs).
-Access to our sources of financing both the investment world and public funds allowing for scalable access to international markets of selected biotechnology sectors.
-Cooperation with defined groups working on legislation related to emerging markets, including in particular the use of in-silico methods and standardization of their use.