DeepMatter own SPRESI, the world’s third largest chemistry reaction database with 4.5 million entries. This hand-curated dataset has been created over the span of 30 years and constitutes chemistry from the past 100 years. It also forms the backbone of our industry leading retrosynthesis tool, ICSynth, which is used worldwide in small-molecule drug discovery.
SmartRecipes for DigitalGlassware is a natural language processing tool that can automatically convert any experimental free text into a highly structured protocol, identifying chemicals, laboratory actions and chemical safety information with a click of a button. Read on to find out more about how SmartRecipes can help you begin digitising your chemistry lab.
Reproducibility in the chemistry lab is an issue that most chemists have experienced in their careers and wastes not only time and money, but can be infuriating when the reasons aren’t clear! DigitalGlassware® from Deepmatter enables a higher level of experimentation through the structuring, collection and collation of relevant chemical data, creating rich run records that leave irreproducibility a thing of the past. Read on to find out more about how DigitalGlassware® can accelerate the digitisation of your data collection in the chemistry lab and lead to improvements in productivity and efficiency.
For the past 10 years ICSYNTH has been the most flexible retrosynthesis software available using the world’s fastest chemical search engine ICFSE. Using our years of experience of working with a host of blue chip pharmaceutical companies, DeepMatter’s new ICSYNTH 4.0 is now even more user friendly. Read on to discover how the new features will enable you to get new synthetic routes to your target compounds faster, better and cheaper.
Convert any lab book or literature procedure into a safe, step by step codified protocol automatically - at the push of a button
DigitalGlassware® automatically creates XML code based on the Recipe the user creates. It means that a chemist with no coding experience can create a comprehensive and complete XML record of their experimental using our Recipe builder tools.
This article highlights the latest release of DigitalGlassware® R2.8.0. The aim of R2.8.0 will be to strengthen and improve the data records captured using the platform to facilitate better insights and analysis.
The addition of outcomes to a run in DigitalGlassware® (and structuring against sensor data) are needed to contextualise and realise the true value of a reaction data set i.e. was this a good data set or a bad data set? This story details the recent update to outcomes, placing the value they provide to data sets and the ability to set Point in Time values.
DeepMatter does more than collect and structure sensor data, we derive insights from your data that you may otherwise miss. An example would be from the DeviceX camera which we can use to quantify colour change and other events in a time-course manner
To facilitate the capture of data from the lab DeepMatter provides several bespoke sensor capturing offerings that come integrated with DigitalGlassware® and are ready to start recording data out of the box: DeviceX and the EnvironmentalSensor
Reaction time observations are typically captured using text or writing down the observation against the experiment record. With DigitalGlassware®, we can captured time stamped notes but also photo notes, meaning ambiguous events such as colour change, precipitation etc can be captured and shared with colleagues.
The ability to persist data on the cloud and maintain version numbers in Recipes allows DigitalGlassware® to keep an archived track of all the users work. This can be accessed immediately and securely through the browser.
RecipeRunner serves multiple roles in the lab including real-time sensor data, capturing notes and guiding you through your Recipe. One role to highlight is the ability to set alerts to sensor data so when the sensor breaches a threshold an alert is fired to the user. Also during Recipe creation there are several tools that can be utilised to help alert the user of risks and places to take care ahead of time (e.g. Caution, Expectations )