Virtually run your experiments at home with machine learning platform Alchemite™ Analytics.
By Dr Andrea Olguin
COVID-19 has forced many establishments to close, including universities, laboratories and manufacturing plants, to name a few. For most people, there is work that can be done at home — but some parts of the scientific process require access to a lab or testing space equipped with the appropriate instruments.
Many scientists, researchers and engineers have had to halt their experiments until further notice. Setbacks in experiments affect innovation and production for those in industry, leading to significant delays in latest battery technologies for example, which could have knock on effects in electrification and ultimately carbon off setting targets. Delays also slow down drug discovery processes or any design process for that matter (i.e. materials and chemicals). Scientists are already forecasting how much they will have to make up for, once labs reopen.
Intellegens is transforming experimental design for companies in drug discovery, aerospace and construction chemicals, enabling a data-driven approach, saving up to 80% of traditional experimental time and cost.
We have come a long way and now we serve several industries — with a mission is to accelerate innovation and production using machine learning. Our Alchemite™ Analytics platform not only offers higher accuracy at greater speed but is also capable of dealing with sparse and noisy data — typical of rare and expensive experimental data.
By predicting and mapping the experimental landscape with attached confidence levels, the approach allows scientists to effectively choose the next best experiment to run in the optimal direction.
Who can benefit
Anyone working with experimental data, such as scientists and engineers working in the R&D sector for the following areas:
Machine Learning for virtual experiments
Machine learning is disrupting traditional R&D and the design of experiments, resulting in significant savings in both time and money in the product development lifecycle.
Traditional experimental design can be used to guide experiments to find the best answer in the shortest number of steps. However, there are a number of problems with this approach, including:
- Experimental and simulation data is sparse and noisy
- Trial and improvements are expensive and time-consuming
- The experimental process is expert-driven and iterative
The development of new methodologies that accelerate the discovery and design of new formulations is therefore crucial for achieving time efficiency and cost reductions.
The advent of machine learning approaches has enabled innovative companies to augment their design of experiments with a more guided approach to not only find the ‘answer’ the quickest but also identify experiments to best improve the underlying model leading to a continual cycle of improved operational performance.
Machine learning is a powerful tool for finding patterns in high-dimensional data. It uses algorithms to learn from experimental data by modelling the relationships and understanding the correlations between properties and related variables.
The benefits of machine learning methods compared to the other designs are largest on high-dimensional data.
Machine learning automates analytical model building using algorithms that iteratively learn from data, thereby allowing computers to discover hidden patterns and insights without being programmed or biased in where to search.
This approach makes minimal assumptions about the systems that are generating the data. It can be effective even when the data are gathered without a controlled experimental design and in the presence of complicated nonlinear interactions.
Historical experimental data as a resource
Today, scientists are turning to their existing data and analysis — since that’s the type of work that can be done computationally at home.
Biopharma companies are reviewing their drug and vaccine databases to see if there is any historical information that could be used to help tackle the novel coronavirus.
Historical experimental data is a valuable resource for businesses, which is often overlooked and difficult to access. Unlocking the knowledge hidden in this data enables companies to stay at the forefront of innovation and optimize future development.
Run your virtual experiments with the Alchemite™ Analytics Platform
Alchemite™ extracts valuable information about correlations between parameters, allowing you to run virtual experiments, saving time and money by eliminating research and development costs.
Traditional R&D is limited by the human inability to interpret multi-dimensional data and make unbiased decisions This combined with the fact that experiments and computational modelling can consume vast quantities of time and resources mean virtual experimentation can not only save time and money but uncover new insights in the data.
Whether you want to discover or optimize new materials or chemicals, the Alchemite™Analytics platform models can also guide you to the next best set of experiments to run to gain an even a better understanding of your experimental landscape.
With virtual experiments, you can:
- Find patterns in high-dimensional data and efficiently deal with rare and expensive data.
- Transform R&D with machine learning by easily experimenting, modelling and visualizing real-world data.
- Choose the next best experiment to run by quickly assessing the accuracy and confidence levels of your results.
Make the most of your time outside the lab by organizing the data you currently have — this exercise will give you the best chance to use machine learning to extract any maximum value from your existing data.
The Alchemite™Analytics platform supports your team’s workflows at each stage of development and lets you visualize the outcomes of time-varying virtual experiments. The Alchemite™ engine is highly optimized, taking minutes to train models that take conventional deep learning methods hours or days.
This fully-fledged and easy-to-use platform (you don’t need to be a data scientist) lets its users manage their models, discover new data points, run predictions, optimizations, and analytics in any type of dataset, with comprehensive confidence measures.
1. Explore the data
Aggregate and explore the experimental data that is being used to train the model. Get insights about each column by easily rendering descriptive statistics (histograms, standard deviation, mean, outliers, etc). Automatically view any imputed values that will be highlighted with attached confidence levels.
Easily predict values for different features with attached uncertainty levels. The ability to present a confidence measure for each prediction enables users to understand how good the prediction is, and then decide if they want to explore safe and confident options or more risky and uncertain opportunities.
This powerful feature allows users to specify the exact target properties a new design should achieve. The user can select from all the parameters and decide if they want to set specific target values that the new design should achieve, this could be minimizing or maximizing or properties or setting fixed values.
4. Analyze and visualize
Analyze, visualize and understand your results. Choose from a variety of different tools and plots to best represent your results.
Some of these tools include sensitivity analyses, property to property plots, and visualization of the ‘predicted’ or ‘designed’ formulation relative to two parameters and in relation to other data that the model knows about.
Quickly check the accuracy and quality of the data and visualize the outcome of real-time process parameter modifications. Turn data into products faster by getting actionable insights through the platform’s interactive dashboards.
Develop models that deliver deeper insights into your experimental data enabling better decision making process, which leads to faster product-to-market.
Take home message
Just because the labs are closed doesn’t not mean that you can’t get ahead with your experiments. Confidently run virtual experiments at home and make use of valuable historical data. Accurate predictions will help you understand which experiments are most likely to yield the best results — saving you both time and money.