BRESSANONE, Italy, July 25, 2022 (GLOBE NEWSWIRE) -- Covision Quality, a leading provider of visual inspection software based on unsupervised machine learning technology, today announced it has joined NVIDIA Metropolis a partner program, application framework, and set of developer tools that bring to market a new generation of vision AI applications that make the worlds most important spaces and operations safer and more efficient.

Covision Qualitys interface from the perspective of the end-of-line quality control operator. In this case, the red border on the image of the manufactured part indicates that the part is not OK, thus can not be sent to the end customer and needs to be discarded.

Thanks to its unsupervised machine learning technology, the Covision Quality software can be trained in an hour on average and generates reduction of pseudo-scrap rates by up to 90% for its customers. Its workstations that are deployed at customer sites harness the power of NVIDIA RTX A5000 GPU-accelerated computing, which allows the software to run in real time processing images, inspecting components, and communicating decisions to the PLC. In addition, Covision Quality leverages NVIDIA Metropolis, the TensorRT SDK, and CUDA software.

NVIDIA Metropolis makes it easier and more cost effective for enterprises, governments, and integration partners to use world-class AI-enabled solutions to improve critical operational efficiency and solve safety problems. The NVIDIA Metropolis ecosystem contains a large and growing breadth of members who are investing in the most advanced AI techniques and most efficient deployment platforms, and using an enterprise-class approach to their solutions. Members have the opportunity to gain early access to NVIDIA platform updates to further enhance and accelerate their AI application development efforts. The program also offers the opportunity for members to collaborate with industry-leading experts and other AI-driven organizations.

Covision Quality is a spin-off of Covision Lab, a leading European computer vision and machine learning application center and company builder. Covision Quality licenses its visual inspection software product to manufacturing companies in several industries, ranging from metal manufacturing to packaging. Customers of Covision Quality include GKN Sinter Metals, a global market leader for sinter metal components, and Aluflexpack Group, a leading international manufacturer of flexible packaging.

Franz Tschimben, CEO of Covision Quality, sees an important value-add in joining the NVIDIA Metropolis program: Joining NVIDIA Metropolis marks yet another milestone in our companys young history and in our relationship with NVIDIA, which started with our company joining the NVIDIA Inception program last year. It is a testament to the great work the team is doing in providing a scalable visual inspection software product to our customers, drastically reducing time to deployment of visual inspection systems and pseudo scrap rates. We expect that NVIDIA Metropolis, which sits at the heart of many developments that are happening in the industry today, will give us a boost in our go-to-market efforts and support us in connecting to customers and system integrators.

About Covision QualityCovision Quality licenses its visual inspection software product to manufacturing companies in several industries, ranging from metal manufacturing to packaging. Thanks to its unsupervised machine learning technology, the Covision Quality software can be trained in an hour on average and generates reduction of pseudo-scrap rates for its customers by up to 90%. Covision Quality is the recipient of the Cowen Startup award at Automate Show 2022 in Detroit, United States.

Covision Quality is a spin-off of Covision Lab, a leading European computer vision and machine learning application center and company builder.For more information, visit http://www.covisionquality.com

Contact information:Covision Qualityhttps://www.covisionquality.com/en 39042 Bressanone, Italy+39 333 4421494info@covisionlab.com

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/19998b6c-83b8-41df-8e60-c5d558e3e408

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Covision Quality joins NVIDIA Metropolis to scale its industrial visual inspection software leveraging unsupervised machine learning - GlobeNewswire

Advanced computer programs influence, and can even dictate, meaningful parts of our lives. Think of streaming services, credit scores, facial recognition software.

As this technology becomes more sophisticated and more pervasive, its important to understand the basic terminology.

People often use algorithm, machine learning and artificial intelligence interchangeably. There is some overlap, but theyre not the same things.

We decided to call up a few experts to help us get a firm grasp on these concepts, starting with a basic definition of algorithm. The following is an edited transcript of the episode.

Melanie Mitchell, Davis professor of complexity at the Santa Fe Institute, offered a simple explanation of a computer algorithm.

An algorithm is a set of steps for solving a problem or accomplishing a goal, she said.

The next step up is machine learning, which uses algorithms.

Rather than a person programming in the rules, the system itself has learned, Mitchell said.

For example, speech recognition software, which uses data to learn which sounds combine to become words and sentences. And this kind of machine learning is a key component of artificial intelligence.

Artificial intelligence is basically capabilities of computers to mimic human cognitive functions, said Anjana Susarla, who teaches responsible AI at Michigan State Universitys Broad College of Business.

She said we should think of AI as an umbrella term.

AI is much more broader, all-encompassing, compared to only machine learning or algorithms, Susarla said.

Thats why you might hear AI as a loose description for a range of things that show some level of intelligence. Like software that examines the photos on your phone to sort out the ones with cats to advanced spelunking robots that explore caves.

Heres another way to think of the differences among these tools: cooking.

Bethany Edmunds, professor and director of computing programs at Northeastern University, compares it to cooking.

She says an algorithm is basically a recipe step-by-step instructions on how to prepare something to solve the problem of being hungry.

If you took the machine learning approach, you would show a computer the ingredients you have and what you want for the end result. Lets say, a cake.

So maybe it would take every combination of every type of food and put them all together to try and replicate the cake that was provided for it, she said.

AI would turn the whole problem of being hungry over to the computer program, determining or even buying ingredients, choosing a recipe or creating a new one. Just like a human would.

So why do these distinctions matter? Well, for one thing, these tools sometimes produce results with biased outcomes.

Its really important to be able to articulate what those concerns are, Edmunds said. So that you can really dissect where the problem is and how we go about solving it.

Because algorithms, machine learning and AI are pretty much baked into our lives at this point.

Columbia Universitys engineering school has a further explanation of artificial intelligence and machine learning, and it lists other tools besides machine learning that can be part of AI. Like deep learning, neural networks, computer vision and natural language processing.

Over at the Massachusetts Institute of Technology, they point out that machine learning and AI are often used interchangeably because these days, most AI includes some amount of machine learning. A piece from MITs Sloan School of Management also gets into the different subcategories of machine learning. Supervised, unsupervised and reinforcement, like trial and error with kind of digital rewards. For example, teaching an autonomous vehicle to drive by letting the system know when it made the right decision like not hitting a pedestrian, for instance.

That piece also points to a 2020 survey from Deloitte, which found that 67% of companies are already using machine learning, and 97% were planning to in the future.

IBM has a helpful graphic to explain the relationship among AI, machine learning, neural networks and deep learning, presenting them as Russian nesting dolls with the broad category of AI as the biggest one.

And finally, with so many businesses using these tools, the Federal Trade Commission has a blog laying out some of the consumer risks associated with AI and the agencys expectations of how companies should deploy it.

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The ABCs of AI, algorithms and machine learning - Marketplace

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Machine learning-based analysis of overall stability constants of metalligand complexes | Scientific Reports - Nature.com

According to the latest research by SkyQuest Technology, the Global Machine Learning Market was valued at US$ 16.2 billion in 2021, and it is expected to reach a market size of US$ 164.05 billion by 2028, at a CAGR of 39.2 % over the forecast period 20222028. The research provides up-to-date Machine Learning Market analysis of the current market landscape, latest trends, drivers, and overall market environment.

Software systems may forecast events more correctly with the use of machine learning (ML), a type of artificial intelligence (AI), without needing to be explicitly told to do so. Machine learning algorithms use historical data as input to anticipate new output values. As organizations adopt more advanced security frameworks, the global machine learning market is anticipated to grow as machine learning becomes a prominent trend in security analytics. Due to the massive amount of data being generated and communicated over several networks, cyber professionals struggle considerably to identify and assess potential cyber threats and assaults.

Machine-learning algorithms can assist businesses and security teams in anticipating, detecting, and recognising cyber-attacks more quickly as these risks become more widespread and sophisticated. For example, supply chain attacks increased by 42% in the first quarter of 2021 in the US, affecting up to 7,000,000 people. For instance, AT&T and IBM claim that the promise of edge computing and 5G wireless networking for the digital revolution will be proven. They have created virtual worlds that, when paired with IBM hybrid cloud and AI technologies, allow business clients to truly experience the possibilities of an AT&T connection.

Computer vision is a cutting-edge technique that combines machine learning and deep learning for medical imaging diagnosis. This has been accepted by the Microsoft InnerEye programme, which focuses on image diagnostic tools for image analysis. For instance, using minute samples of linguistic data, an AI model created by a team of researchers from IBM and Pfizer can accurately forecast the eventual onset of Alzheimers disease in healthy persons by 71 percent (obtained via clinical verbal cognition tests).

Read Market Research Report, Global Machine Learning Market by Component, (Solutions, and Services), Enterprise Size (SMEs And Large Enterprises), Deployment (Cloud, On-Premise), End-User [Healthcare, Retail, IT and Telecommunications, Banking, Financial Services and Insurance (BFSI), Automotive & Transportation, Advertising & Media, Manufacturing, Others (Energy & Utilities, Etc.)], and Region Forecast and Analysis 20222028 By Skyquest

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Large enterprises segment dominated the machine learning market in 2021. This is because data science and artificial intelligence technologies are being used more often to incorporate quantitative insights into business operations. For instance, under a contract between Pitney Bowes and IBM, IBM will offer managed infrastructure, IT automation, and machine learning services to help Pitney Bowes convert and adopt hybrid cloud computing to support its global business strategy and goals.

Small and midsized firms are expected to grow considerably throughout the anticipated timeframe. It is projected that AI and ML would be the main technologies allowing SMEs to reduce ICT investments and access digital resources. For instance, the IPwe Platform, IPwe Registry, and Global Patent Marketplace are just a few of the small- and medium-sized firms (SMEs) and other organizations that are reportedly already using IPwes technology.

The healthcare sector had the biggest share the global machine learning market in 2021 owing to the industrys leading market players doing rapid research and development, as well as the partnerships formed in an effort to increase their market share. For instance, per the terms of the two businesses signed definitive agreement, Francisco Partners would buy IBMs healthcare data and analytics assets that are presently a part of the Watson Health company. An established worldwide investment company with a focus on working with IT startups is called Francisco Partners. Francisco Partners acquired a wide range of assets, including Health Insights, MarketScan, Clinical Development, Social Program Management, Micromedex, and imaging software services.

The prominent market players are constantly adopting various innovation and growth strategies to capture more market share. The key market players are IBM Corporation, SAP SE, Oracle Corporation, Hewlett Packard Enterprise Company, Microsoft Corporation, Amazon Inc., Intel Corporation, Fair Isaac Corporation, SAS Institute Inc., BigML, Inc., among others.

The report published by SkyQuest Technology Consulting provides in-depth qualitative insights, historical data, and verifiable projections about Machine Learning Market Revenue. The projections featured in the report have been derived using proven research methodologies and assumptions.

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SkyQuest has Segmented the Global Machine Learning Market based on Component, Enterprise Size, Deployment, End-User, and Region:

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Key Players in the Global Machine Learning Market

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Global Machine Learning Market is Expected to Grow at a CAGR of 39.2 % by 2028 - Digital Journal

All the experiments were conducted at the Agri-Food and Biosciences Institute (AFBI) farm at Hillsborough, County Down, UK. All the experiments and procedures complied with the requirements of the UK Animals (Scientific Procedures) Act 1986 and were approved by the AFBI Hillsborough Ethical Review Group. All the experiments were performed in accordance with relevant guidelines and regulations (following the ARRIVE guidelines26).

Data used were collated from 43 total diet digestibility studies with 951 lactating dairy cows undertaken at Agri-Food and Biosciences Institute in Northern Ireland over a period of 26years (19902015). The data from studies undertaken between 1990 and 2002 were used as the training dataset (n=564) and undertaken between 2005 and 2015 as the testing dataset (n=387). The training data were used to develop prediction models for MN using MLR and the three selected machine learning algorithms (ANN, RFR and SVR). These new models were then tested for their predictive performance using the training dataset by tenfold cross validation. The testing dataset were used for the independent evaluation and comparison of predictive ability of different modeling approaches. The information of the two datasets on numbers of experiments, cow genotypes and forage types offered are presented in Table 10. Data on live weight, milk production, feed intake, N intake and outputs are presented in Table 11. The datasets used in the present study showed a various cow genetic merit and a broad range in LW (379781kg), MY (5.140.2kg/d), total dry matter intake (7.5426.6kg/d), FP (0.211.00%), DNC (19.038.0g/kg DM), diet metabolizable energy concentration (DMEC, 9.6819.4MJ/kg DM) and NI (155874g/d), which represents typical dairy production conditions managed within grassland-based dairy systems in the West and North Europe.

Cows were housed in free-stall cubicle accommodation for at least 20 d before commencing digestibility trials in metabolism units for 8 d with feed intake, milk production and feces and urine collected during the final 6 d. Throughout the whole experiment, cows were offered experimental diets ad libitum and had free access to water. During the final 6 d, the following measurements for each individual cows were carried out to generate total digestibility data used in the present study. Forages and concentrates offered and refused were recorded daily and sampled for analysis of feed dry matter (DM), N concentration and forage proportion. Feces and urine outputs were collected daily and sampled for DM (feces only) and N concentration. Milk yield was recorded daily and sampled for analysis fat, protein and lactose concentrations. Live weight was measured on the first and last days in the metabolism unit. Details in feed intake, feces and urine collection and methods used for analysis of feed, feces, urine and milk samples were described by Yan et al.6.

Because features (variables) in raw data may have different dynamic ranges, which may result in poor model performance, it is recommended to normalize them to make ANN training more efficient by performing normalization process for the raw inputs10. In the present study, all the input data for ANN models were normalized into the interval [0, 1] by performing MinMax normalization technique27 using Eq.(1):

$$X_{norm} = frac{{X {-} X_{min} }}{{X_{max} - X_{min} }}$$

(1)

where Xnorm or X is the normalized or original value, Xmin or Xmax is the minimum or maximum values of the input data.

After finding the optimal tuning parameter, all normalized data for MN obtained by ANN models were denormalized into their original scale using Eq.(2) 27:

$$Y = Y_{norm} * , left( {Y_{max} - , Y_{min} } right) , + , Y_{min}$$

(2)

where Ynorm or Y is the normalized or demoralized value, Ymin or Ymax is the minimum or maximum values of the output data.

Feature selection is an essential step during development of models, which can hugely impact the generalization and predictive ability of models10,28. In the present study, a hybrid knowledge-based and data driven approach was developed and implemented to selecting features. Knowledge in animal science and the process of digestibility trial were applied to diagnosing and removing irrelevant features before the implementing of data driven feature selection process. For instance, the features of feces N output (FN) and urine N output (UN) were excluded from the set of features in present study according to prior background and expert knowledge. Because the data of UN and FN were obtained from analyzing urine and feces samples and then they were summed up and treated as new feature MN, both FN and UN are heavily correlated with MN. Their inclusion in the features list might cause poor generalization performance of the models. Furthermore, the optimal features selected from data driven approach may need to be diagnosed based on background knowledge in animal science according to the scenarios of model application. For instance, several variables (e.g. NI and FP) included in datasets used in this study may not be available in commercial farms. Therefore, alternative feature (concentrate dry matte intake, CDMI) was selected and included into the feature list in this study based on the domain knowledge and then new ANN model suits for commercial farms was developed.

The filter method was applied for feature selection using the Pearson correlation matrix and variance inflation factor (VIF) technique. The first step was to use the Pearson correlation matrix to identify features which might correlate each other for prediction of MN excretion, because using correlated features in models could influence performance of these models with a biased outcome. If two features were heavily correlated, the less important one was removed from the set of features to minimize adverse effects on model performance. Afterwards, the VIF analysis was applied to detect multicollinearity, which has been widely used as a measure of the degree of multicollinearity among input features. A VIF score was calculated for each feature and those with high values were removed. The threshold score for the VIF analysis was 5 and features with a VIF score below this threshold were selected. The VIF score was computed by VIF function in R29.

In the present study, four models based on the MLR ANN, RFR and SVR were developed using the training dataset and these new models were tested using the testing dataset for comparison of their prediction performance for MN outputs in lactating dairy cows (presented later). The MLR with the stepwise procedure for selection of independent variables was used as benchmark model since it is a well-known technique and has been applied for modelling in a wide range of applications in animal science research. Alternative modeling approaches proposed in the present study were ANN, RFR and SVR. To compare the performance, models developed with different approaches and ensure that the same resampling sets were used between calls, the same random number seeds were set prior to perform the process of training, fitting and testing models. All statistical analyses were performed with R29.

The MLR model (Eq.3) selected in the present study for the prediction of MN output was published in 20066 which was developed using the same training dataset listed in Table 2. To improve the estimation of the regression parameters, experiment was included as a random factor during the development of MLR model. The dataset had a large range within each dependent or independent variable, e.g., MN, NI, LW, MY, FP and DNC, which is vital to ensure the development of robust regression model applicable under various farming conditions10.

$${text{MN }}left( {{text{g}}/{text{d}}} right) , = , 0.{text{749 NI }} + , 0.0{text{65 LW }}{-}{ 1}.{text{515 MY }}{-}{ 17}.0$$

(3)

where NI, LW and MY are N intake (g/d), live weight (kg) and milk yield (kg/d), respectively.

In the present study, ANN was fitted using R package neuralnet which was built to train neural networks in the context of regression analyses. The details of ANN training and application of neuralnet were described by Gnther and Fritsch30. Multilayer perceptron networks trained with backpropagation learning algorithms were used and consist of an input layer, hidden layer(s) and an output layer. The input variables were obtained by using the feature selection algorithm described in the section Knowledge-based and data driven feature selection, and the neuron in output layer represents MN. The ANN models were trained based on the selection of training algorithms and learning parameters including the number of hidden layer(s), number of neurons in hidden layer(s), error function, threshold for partial derivatives of the error function as stopping criteria, and activation function etc.. The optimized number of hidden layer(s), number of neuron(s) in the hidden layer(s), learning algorithms, learning rate and other learning parameters were obtained on the basis of prediction performance measured as relative root mean square error (RRMSE, Eq.6) with tenfold cross validation and then the best topology/architecture was finalized.

The RFR is an ensemble machine learning method and a nonparametric technique derived from classification and regression trees which are constructed using a bootstrap aggregating (bagging) method from the training data31. In RFR, prediction is conducted by averaging the individual tree predictions. A detailed description of RFR theory can be found in the report by Breiman32. The RFR was implemented by the randomForest function in the R package (version 3.6.1). To select the optimal hyperparameters for learning algorithm, tuning process was performed based on the R package ranger. The hyperparameters include number of trees to grow (ntree), number of randomly drawn candidate variables (mtry), sample size and node size. Grid search strategy was used to choose the candidate hyperparameter values and the performances of the trained algorithm with different values of the hyperparameters were evaluated as RRMSE (Eq.6) by using tenfold cross validation.

The SVR uses similar principles as support vector machine, a supervised non-parametrical statistical learning technique that uses the kernel functions and the maximum margin algorithm to solve the nonlinear problem33. The detailed theoretical background and description of SVR can be found in the report by Cristianini and Shawe-Taylor34. The SVR model performs the regression estimation by risk minimization where the risk is measured by a loss function. In this study, R package e1071 was used and the svm function was implemented to fit SVR model. The radial basis kernels function, the most commonly used kernels types, was employed in training and predicting process. Parameter tuning was performed by using grid search over supplied parameter ranges and the best combination of parameters (lowest RMSE) were selected. The performance of SVR model was measured as RRMSE (Eq.6) with tenfold cross validation.

The MLR model and the three new models (ANN, RFR and SVR) was developed and compared in terms of their prediction performance for MN outputs in lactating dairy cows based on the datasets listed in Table 2. The predictive performance of models were evaluated using coefficient of determination (R2), root mean square error (RMSE), relative root mean square error (RRMSE) and concordance correlation coefficient (CCC), based on the actual and predicted values. The R2 was calculated using Eq.(4). The RMSE and RRMSE were produced in a tenfold cross validation process (10 RMSE data generated) using Eq.(5)35 and Eq.(6)36, respectively. The concordance correlation coefficient (CCC), a further measure of the agreement between observed and predicted values, was given by Eq.(7)37. The tenfold cross validation was used to evaluate prediction performance of these models (MLR, ANN, RFR and SVR)The obtained RMSE, RRMSE and CCC values (n=10) through the tenfold cross validation were compared among the 4 models using one-way analysis of variance and then followed by Tukeys honest significant difference (HSD) test (=0.05). The same cross validation folds were used for all modeling scenarios to compare cross all of the models performance.

$$R^{2} = 1 - frac{{sum left( {y_{i} - hat{y}} right)^{2} }}{{sum left( {y_{i} - overline{y}} right)^{2} }}$$

(4)

$$RMSE = sqrt { frac{1}{n}mathop sum limits_{i = 1}^{n} left( {y_{i} - hat{y}} right)^{2} }$$

(5)

$$RRMSE = (RMSE/overline{y}) times , 100$$

(6)

$$CCC = frac{{2 cdot ,r cdot ,S_{{widehat{y}}} cdot,S_{y} }}{{S_{{widehat{y}}}^{2} + S_{y}^{2} + left( {mathop sum nolimits_{i = 1}^{n} frac{{left( {y_{i} - widehat{y}} right)}}{n}} right)^{2} }}$$

(7)

where (y_{i}) is actual MN, (widehat{{y_{i} }}) is predicted MN, (overline{y}) is the mean of actual MN and n is the number of observations, r is the Pearson correlation coefficient between (widehat{{y_{i} }}) and (overline{y}), (S_{{hat{y}}}) and (S_{y}) are the respective standard divisions.

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Can machine learning algorithms perform better than multiple linear regression in predicting nitrogen excretion from lactating dairy cows | Scientific...

Scientists have developed artificial intelligence software that can create proteins that may be useful as vaccines, cancer treatments, or even tools for pulling carbon pollution out of the air.

This research, reported today in the journal Science, was led by the University of Washington School of Medicine and Harvard University. The article is titled"Scaffolding protein functional sites using deep learning."

The proteins we find in nature are amazing molecules, but designed proteins can do so much more, said senior author David Baker, an HHMI Investigator and professor of biochemistry at UW Medicine. In this work, we show that machine learning can be used to design proteins with a wide variety of functions.

For decades, scientists have used computers to try to engineer proteins. Some proteins, such as antibodies and synthetic binding proteins, have been adapted into medicines to combat COVID-19. Others, such as enzymes, aid in industrial manufacturing. But a single protein molecule often contains thousands of bonded atoms; even with specialized scientific software, they are difficult to study and engineer.

Inspired by how machine learning algorithms can generate stories or even images from prompts, the team set out to build similar software for designing new proteins. The idea is the same: neural networks can be trained to see patterns in data. Once trained, you can give it a prompt and see if it can generate an elegant solution. Often the results are compelling or even beautiful, said lead author Joseph Watson, a postdoctoral scholar at UW Medicine.

The team trained multiple neural networks using information from the Protein Data Bank, which is a public repository of hundreds of thousands of protein structures from across all kingdoms of life. The neural networks that resulted have surprised even the scientists who created them.

The team developed two approaches for designing proteins with new functions. The first, dubbed hallucination is akin to DALL-E or other generative A.I. tools that produce new output based on simple prompts. The second, dubbed inpainting, is analogous to the autocomplete feature found in modern search bars and email clients.

Most people can come up with new images of cats or write a paragraph from a prompt if asked, but with protein design, the human brain cannot do what computers now can, said lead author Jue Wang, a postdoctoral scholar at UW Medicine. Humans just cannot imagine what the solution might look like, but we have set up machines that do.

To explain how the neural networks hallucinate a new protein, the team compares it to how it might write a book: You start with a random assortment of words total gibberish. Then you impose a requirement such as that in the opening paragraph, it needs to be a dark and stormy night. Then the computer will change the words one at a time and ask itself Does this make my story make more sense? If it does, it keeps the changes until a complete story is written, explains Wang.

Both books and proteins can be understood as long sequences of letters. In the case of proteins, each letter corresponds to a chemical building block called an amino acid. Beginning with a random chain of amino acids, the software mutates the sequence over and over until a final sequence that encodes the desired function is generated. These final amino acid sequences encode proteins that can then be manufactured and studied in the laboratory.

The team also showed that neural networks can fill in missing pieces of a protein structure in only a few seconds. Such software could aid in the development of new medicines.

With autocomplete, or Protein Inpainting, we start with the key features we want to see in a new protein, then let the software come up with the rest. Those features can be known binding motifs or even enzyme active sites, explains Watson.

Laboratory testing revealed that many proteins generated through hallucination and inpainting functioned as intended. This included novel proteins that can bind metals as well as those that bind the anti-cancer receptor PD-1.

The new neural networks can generate several different kinds of proteins in as little as one second. Some include potential vaccines for the deadly respiratory syncytial virus,orRSV.

All vaccines work by presenting a piece of a pathogen to the immune system. Scientists often know which piece would work best, but creating a vaccine that achieves a desired molecular shape can be challenging. Using the new neural networks, the team prompted a computer to create new proteins that included the necessary pathogen fragment as part of their final structure. The software was free to create any supporting structures around the key fragment, yielding several potential vaccines with diverse molecular shapes.

When tested in the lab, the team found that known antibodies against RSV stuck to three of their hallucinated proteins. This confirms that the new proteins adopted their intended shapes and suggests they may be viable vaccine candidates that could prompt the body to generate its own highly specific antibodies. Additional testing, including in animals, is still needed.

I started working on the vaccine stuff just as a way to test our new methods, but in the middle of working on the project, my two-year-old son got infected by RSV and spent an evening in the ER to have his lungs cleared. It made me realize that even the test problems we were working on were actually quite meaningful, said Wang.

These are very powerful new approaches, but there is still much room for improvement, said Baker, who was a recipient of the 2021 Breakthrough Prize in Life Sciences. Designing high activity enzymes, for example, is still very challenging. But every month our methods just keep getting better! Deep learning transformed protein structure prediction in the past two years, we are now in the midst of a similar transformation of protein design.

This project was led by Jue Wang, Doug Tischer, and Joseph L. Watson, who are postdoctoral scholars at UW Medicine, as well as Sidney Lisanza and David Juergens, who are graduate students at UW Medicine. Senior authors include Sergey Ovchinnikov, a John Harvard Distinguished Science Fellow at Harvard University, and David Baker, professor of biochemistry at UW Medicine.

Compute resources for this work were donated by Microsoft and Amazon Web Services.

Funding was provided by the Audacious Project at the Institute for Protein Design; Microsoft; Eric and Wendy Schmidt by recommendation of the Schmidt Futures; the DARPA Synergistic Discovery and Design project (HR001117S0003 contract FA8750-17-C-0219); the DARPA Harnessing Enzymatic Activity for Lifesaving Remedies project (HR001120S0052 contract HR0011-21-2-0012); the Washington Research Foundation; the Open Philanthropy Project Improving Protein Design Fund; Amgen; the Human Frontier Science Program Cross Disciplinary Fellowship (LT000395/2020-C) and EMBO Non-Stipendiary Fellowship (ALTF 1047-2019); the EMBO Fellowship (ALTF 191-2021); the European Molecular Biology Organization (ALTF 139-2018); the la Caixa Foundation; the National Institute of Allergy and Infectious Diseases (HHSN272201700059C), the National Institutes ofHealth (DP5OD026389); the National Science Foundation (MCB 2032259); the Howard Hughes Medical Institute, the National Institute on Aging (5U19AG065156); the National Cancer Institute (R01CA240339); the Swiss National Science Foundation; the Swiss National Center of Competence for Molecular Systems Engineering; the Swiss National Center of Competence in Chemical Biology; and the European Research Council(716058).

Written by Ian Haydon, UW Medicine Institute for Protein Design

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Biologists train AI to generate medicines and vaccines - UW Medicine Newsroom