1. Machine Learning-Guided Directed Evolution for Protein Engineering

Overview

• Machine learning-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. Machine learning methods use data to predict sequence maps to function without requiring a detailed model of the underlying physics or biological pathways.

Company/Brand involved

• N/A

Success Metrics

• Machine learning methods accelerate directed evolution by learning from information contained in all measured variants and uses that information to select sequences that are likely to be improved.

Additional Insights

• In 2019, Denovium and Maxygen entered into a partnership to apply AI in directed evolution for protein engineering. This partnership brings together Maxygen’s expertise in molecular breeding and directed evolution with Denovium’s state-of-art AI engine.

2. Machine Learning-Assisted Directed Protein Evolution with Combinatorial Libraries

Overview

• To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, machine learning is incorporated into the directed evolution workflow.
• Combinatorial sequence space can be quite expensive to sample experimentally but machine learning models trained on tested variants provide a fast method for testing sequence space computationally.

Company/Brand involved

• N/A

Success Metrics

• Incorporating machine learning into directed evolution workflow helps to reduce associated expenses in performing experiments in combinatorial sequence space.
• By greatly increasing throughput within silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.

RESEARCH STRATEGY

Strengths

• Machine learning-guided directed evolution is groundbreaking in the biological design field as it enables complex function optimization. Machine learning methods predict how sequence maps function, without the need for detailed models of biological pathways or underlying physics.
• Directed evolution alone is time-, energy- and material consuming, regardless of the diversification technique that is used. Machine learning methods intelligently select new variants that can reach higher fitnesses and speed up the direct evolution process, in comparison to directed evolution performed without AI.
• Database of Structural Propensities of Proteins (dSPP), the world's first interactive repository of dynamic and structural features of proteins, has been opened up to enable researchers and protein engineers to utilize and integrate artificial intelligence and machine learning frameworks such as Tensorflow and Keras.

Weaknesses

• Machine learning has limitations and may not necessarily be useful in all applications. Machine learning is useful in cases of shortage in high-throughput screen limits, but if the number of sequences that can be screened is sufficient, or if the fitness landscape is additive and smooth, machine learning may not succeed in finding better variants or decreasing screening burden.
• Machine learning methods cause expenses to increase, especially in DNA sequencing and computation. Though the cost of using machine learning in directed evolution is decreasing, protein engineers are advised to keep the excessive costs in mind, especially if machine learning is not necessarily required.
• Despite various possibilities, machine learning techniques have another limitation, in that the AI processes need large amounts of data to be effective.

Opportunities

• There are fields where machine learning can improve. Machine learning can take advantage of analyzing unlabeled protein sequences that are not of specific interest to the protein engineer, in order to eventually discover functional or structural information that might lead to novel protein functions. This was confirmed by Frances Arnold, a Nobel-winning chemical engineer, as she mentioned this in one of her research papers.
• According to an article written by Dr. James Canton, directed evolution will become shaped by AI, starting from 2020.

Threats

• A terrorism risk is associated with AI application in directed evolution. In 2013, a committee headed by the Defense Intelligence Agency (DIA) was formed to warn US policymakers of the potential bioterrorism threat of developing viral and biosynthetic pathogens.
•  A recent study confirmed that directed evolution can be exploited for development of bioterrorism agents. Precisely, the study noted that the available variants of anthrax pathogen may be developed and carried out for negative purposes, with help of database libraries.

RESEARCH STRATEGY

AI Application in Biotechnology Trends

Disease Identification

• Overview Of The Trend : AI with Biotechnology has been shown to have a greater chance of correctly identifying diseases in patients.

• About The Trend : Year over year, the industries of AI and biotechnology for disease identification continue to achieve greater influence in AI and medical publishings.

• The Driver of The Trend : The correct identification and diagnosis of a disease is one of the biggest challenges in medicine, and that is why it has been a number one priority one machine learning development within the biotechnology industry.

• Impact of The Trend : AI with Biotechnology Disease Identification has already changed the way Sanfilippo Syndrome has been diagnosed, with the AI correctly identifying the disease 90% of the time, outperforming the clinical experts on three experiments.

• Companies Within The Trend : IBM has partnered with Watson Health in order to develop a machine learning technology for the purposes of disease identification the IBM Watson Genomics uses AI to increase the accuracy of cancer identification.
• Companies Within The Trend: DeepMind Health is owned by Google and P1vital and has been collaborating with the University of Oxford researchers in order to perform and perfect the AI biotechnology of PreDicT, which is focused on mental health and depression.

Drug Discovery and Manufacturing

• Overview of The Trend : Artificial intelligence, machine learning, and computing technologies are making drug discovery cheaper and quicker.
• About The Trend : Pharmaceutical companies are no longer trying to attract doctors offices, but rather go directly to the consumers, in turn, are paying more attention to the pharmaceutical companies, and what they are putting in their bodies. This has led to the era of personalized medications, which have had a positive impact on health, with more and more companies seeking a way to produce those in an easier and quicker way.
• Drivers of The Trend : Most devastating diseases and the companies who strive to cure them face debilitating crosshairs such as soaring drug-discovery costs and testing times. AI and biotechnology is being used to create a faster and more efficient way to find and develop new drugs.
• Impact of The Trend : 70% of pharmaceutical companies agree that AI will play a big part in their work and over 60% of the companies already invest in AI.
• Companies in The Trend : MIT Clinical Machine Learning Group, focuses on algorithm development within the drug discovery and manufacturing industry in regards to AI and biotechnology.
• Companies in The Trend: Microsoft also has a project, known as Project Hanover which uses AI in order to develop a personalized drug protocol in the hopes of managing acute myeloid leukemia.

Imaging

• Overview of The Trend : AI, when used with biotechnology, has shown to have a positive impact on machine imaging in the healthcare field with such developments shown to match or exceed the accuracy of human experts when analyzing images.

• About The Trend : Interest for the AI and Biotechnology trend of imaging has increased over the past 12–18 months, especially in the radiology field with the industry expected to increase to $2 billion by 2023. 

• Drivers of The Trend : In medical imaging, such as radiology, images are viewed by trained physicians in order to detect, and monitor diseases. This is a time-consuming process that leaves room for error. There has been a need for a faster, more quantitative option, and AI, along with biotechnology, offers this.

• Impact of The Trend : There has been an enhanced productivity in the imaging field as well as an overall increased diagnostic accuracy. The use of AI in hand with biotechnology has given the healthcare imaging field the ability to give more personalized treatment planning, and ultimately, a better and more enhanced clinical outcome for patients.

• Companies Within The Trend : GE Healthcare, along with Macquarie University and Macquarie Medical Imaging, collaborated in order to diagnose and monitor brain aneurysms on medical images faster and more efficiently using AI.

• Companies Within The Trend : Biocellvia, a company in France, offers automated digital image analysis and claims that their test results bring clearer insights for patients and stakeholders and are quicker than other tests.