In 2020, 80% of executives in the chemicals industry believed that AI would become important to the success of their business in the next three years. Now, as we settle into 2023, this majority has been proven right as the uses of artificial intelligence and machine learning continue to grow within chemistry. But is the industry making the most of what AI can do?

New technologies are being released all the time, each offering their own possibilities for furthering the advancement of chemical experimentation. Although these platforms may differ in exact methodologies, the common goals of implementing AI in chemistry can be boiled down to three main aims: acceleration, optimisation and accuracy.

Read on to discover how AI is set to impact these three elements of experimentation, and how SmartChemistry^® from deepmatter^® can help to bring these solutions to your lab.

1: Acceleration

One of the top reasons AI is used in any industry is to accelerate a specific process or task, and this is no different in chemistry. Drug discovery, for example, offers a great opportunity for machine learning to elevate the speed of delivery.

Developing novel drugs takes time, but part of the reason for this is the level of experimentation involved. Finding new routes to synthesis requires thousands of individual tests, which each need to be analysed to provide learnings for the next attempt. When completed manually, this process is incredibly time-consuming and the learnings are slow to appear.

With the help of machine learning, however, chemists can optimise the development stage by running possible combinations at a much greater speed, analysing the outcomes through AI predictions and ensuring the process is as efficient as possible. AI can generate novel concepts for synthesis quickly, identifying potential routes and enabling further experimentation at a higher speed than scientists could using manual methods.

With most drugs taking 10 years or more  to go from development to circulation, the use of AI during the discovery stage can significantly reduce timescales and ensure that lifesaving drugs are developed quickly, safely, and distributed to those who need them.

SmartChemistry^®, for example, uses advanced synthesis design to quickly produce novel concepts that enable chemists to develop synthetic routes much faster than before.

2: Optimisation

Experimentation often comes hand-in-hand with waste, as the nature of the exercise means that a large proportion will be unsuccessful and the materials will be lost. It can be hard to predict the volume of materials required, leading to under- or over-purchasing resources and dealing with the waste that comes from this.

However, AI can help optimise the use of raw materials and purchasing decisions throughout the process. The programs can predict how much material is required, saving you money and resources without compromising on the experimentation stage.

As well as predicting resources, machine learning programs can also offer predictions based on your past data that highlight optimal experimental conditions. This can streamline the way chemists experiment and eliminate unnecessary testing.

Plus, AI that integrates with data collection tools like SmartChemistry^® can provide real time data analysis to offer the insights you need as the experiment occurs, reducing down time and allowing chemists to work more efficiently. The structured data captured can then be applied in ML-models to optimise the outcomes of the experimentation process - from yield and purity to formulations.

3: Accuracy

Even the most accomplished chemists can make mistakes - especially when dealing with large data sets from thousands of experiments. But with the help of AI automating your calculations and analysis, you can trust that the data being recorded is accurate and up to date.

With SmartChemistry^®, the automated data is stored for future experiments, always available for review, download and analysis thanks to its Cloud-based approach. This means that your lab work is correct, accessible and ready for you to use, whenever you need it.

The future of AI in drug discovery

The position of machine learning in chemistry is still in its infancy, but this doesn’t mean that it isn’t yet helpful or able to provide an advantage for your experiments. Developing novel routes for drug synthesis is a long, complex process, but advancements in AI can help to ensure that your lab work is efficient, optimised and accurate.

To find out more about how SmartChemistry^® from deepmatter^® can elevate your chemical experimentation, contact us here