04/09/2020 – AI3SD Online Seminar Series: Machine Learning for Early Stage Drug Discovery – Professor Charlotte Deane

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Professor Charlotte Deane from the University of Oxford speaks about some of the work her research group have done on Machine Learning for Early Stage Drug Discovery to give a flavour of the different kinds of approaches they have been looking at. These run from predicting whether molecules will bind or not bind to a given protein target, to trying to remove biases from that kind of work, to finally how do we generate novel molecules in the protein binding sites. 

Continue Reading04/09/2020 – AI3SD Online Seminar Series: Machine Learning for Early Stage Drug Discovery – Professor Charlotte Deane

04/09/2020 – AI3SD Online Seminar Series: Machine Learning for Early Stage Drug Discovery – Professor Charlotte Deane

https://www.youtube.com/watch?v=GY0myVuhrCo&t=14s&ab_channel=AI4ScientificDiscovery Abstract: Professor Charlotte Deane from the University of Oxford speaks about some of the work her research group have done on Machine Learning for Early Stage Drug Discovery to…

Continue Reading04/09/2020 – AI3SD Online Seminar Series: Machine Learning for Early Stage Drug Discovery – Professor Charlotte Deane

15/07/2020 – AI3SD Online Seminar Series: InChI: measuring the molecules – Professor Jonathan Goodman

https://www.youtube.com/watch?v=B881b5BasnU&ab_channel=AI4ScientificDiscovery Interview: Prior to this seminar, AI3SD interviewed Jonathan. This interview can be found here: https://eprints.soton.ac.uk/442343/ Abstract: The IUPAC international chemical identifier, InChI, provides a way to name molecules. It is defined…

Continue Reading15/07/2020 – AI3SD Online Seminar Series: InChI: measuring the molecules – Professor Jonathan Goodman

15/07/2020 – AI3SD Online Seminar Series: InChI: measuring the molecules – Professor Jonathan Goodman

The IUPAC international chemical identifier, InChI, provides a way to name molecules. It is defined by an open algorithm that transforms molecular structures into unique strings of text. Each molecule should have exactly one InChI, and each InChI should correspond to exactly one molecule. This property makes it a useful tool in the management of chemical information, and it is widely used. The InChI Trust and IUPAC are continuing to work on developing the standard and on creating new tools which are built on the InChI. This talk will outline how the InChI is used now, and how this may develop in the future.

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07/07/2020 – AI4Good @ WebSci20

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This year the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) will be running a workshop at the WebSci20 Conference in Southampton, UK. Artificial and Augmented Intelligence systems have the potential to make a real difference in the scientific discovery domain however this brings a new wealth of ethical and societal implications to consider with regards to this research (e.g. human enhancement, algorithmic biases, risk of detriment). This workshop looks to explore the ethical and societal issues centered around using intelligent technologies (Artificial Intelligence, Augmented Intelligence, Machine Learning, and in general Semantic Web Knowledge Technologies) to further scientific discovery, with a strong consideration of data ethics and algorithmic accountability. Advances in technology and software are rarely inherently bad in themselves, however that unfortunately does not preclude them from being subverted to ill intent by others; furthermore, as demonstrated by the examples above, even an unintentional lack of care towards ethical codes and algorithmic accountability can lead to societal and ethical implications of scientific discovery. It is our responsibility as researchers to consider these issues in our research; are we conducting studies ethically? What ethical codes can we put in place for scientific discovery research to mitigate against ethical and societal issues. These are really important issues, and they require an interdisciplinary focus between scientists, social scientists and technical experts in order to be comprehensively addressed.

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9-11/03/2020 – AI3SD, Dial-a-Molecule & Directed Assembly: AI for Reaction Outcome and Synthetic Route Prediction – DeVere Tortworth Court Hotel, Gloucestershire

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This is a joint meeting between the Dial-a-Molecule, Directed Assembly and AI3SD (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) Networks. The meeting will examine the state of the art and future opportunities in the use of Artificial Intelligence to predict the outcome of unknown chemical reactions, and consequently design optimum synthetic routes to desired molecules. A wide variety of AI approaches will be illustrated including expert systems, statistical methods, mechanism based and Machine Learning. The meeting will also consider: Data sourcing, sharing, and quality. Automated experimentation to generate reaction knowledge. Theoretical calculations to enrich or replace experimental data. The meeting will include talks to introduce the breadth of the area to all participants. Discussion sessions and opportunities to develop collaborations will be a key aspect of the meeting.

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31/01/2020 – AI3SD, OSM & RSC-CICAG: AI and ML in Drug Discovery: Predicting Bioactive Molecules when there is No Target

Back in January 2019 AI3SD announced their first funding call. We funded 3 pilot projects, and one of the successful applicants of this funding call was Professor Mat Todd from…

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31/01/2020 – AI3SD, OSM & RSC-CICAG: AI and ML in Drug Discovery: Predicting Bioactive Molecules when there is No Target

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This one-day meeting concerns the application of machine learning/artificial intelligence (ML/AI) approaches to the discovery of new drug leads. Specifically the meeting is about cases where the biological target is not clearly established - so-called phenotypic drug discovery. The meeting centers on a real example - a competition run by Open Source Malaria (OSM), funded by a grant from the EPSRC/AI3SD+ Network. Data on active and inactive compounds in one OSM antimalarial series were published online, and anyone was able to submit a model able to predict the actives. The models were judged against a dataset that was kept private, and the winners were asked to use their models to predict novel molecules. These are currently being made in the lab and biologically evaluated, and the results will be reported at the meeting, providing a real-world test, and a complete case study, of the capabilities of ML/AI approaches to accelerate modern drug discovery. We will hear from some of the eleven competition entrants about how their models were constructed, and will have other presentations on related developments. We hope during this meeting to establish which approaches worked well, which did not, and why. All those interested in the application of ML/AI methods to drug discovery are encouraged to attend. The meeting is free, but there will be a cap on numbers, meaning first come first served, meaning registration is essential. Lunch will be provided as part of this event. GitHub Repository: https://github.com/OpenSourceMalaria/Series4_PredictiveModel

Continue Reading31/01/2020 – AI3SD, OSM & RSC-CICAG: AI and ML in Drug Discovery: Predicting Bioactive Molecules when there is No Target