16/09/2020 – AI3SD Online Seminar Series: Supramolecular Antimicrobials – the next target for AI/Machine Learning? – Dr Jennifer Hiscock

  • Post author:
  • Post category:

Since the 1980’s the development of novel antibiotics has dramatically reduced. This, combined with the ever-increasing prevalence of antibiotic resistance in bacteria, means that some bacterial strains have now been identified that are resistant to treatment with all known classes of antibiotic currently available. Supramolecular Self-associating Amphiphiles (SSAs) are a novel class of amphiphilic salts that contain an uneven number of covalently linked hydrogen bond donating and accepting groups, meaning that they are ‘frustrated’ in nature. The hydrogen-bonded, self-associative properties for members of this class of over 70 compounds synthesised to date have been extensively studied in the gas phase, solution state, solid state and in silico. Through these studies we have shown correlations between certain physicochemical properties that maybe predicted by simple, low-level, high-throughput, easily accessible computational modelling. In addition, members from this class of compound have been shown to kill a variety of different bacteria, including those with known antibiotic resistance (e.g. Methicillin Resistant Staphylococcus aureus (MRSA)). These initial studies have highlighted within the supramolecular chemistry community a vast amount of experimental data, not yet accessed by AI/machine learning. Could data sets such as these be the next targets of interest for this community? Is there room for a consortium or community led approach to solving predictive modelling within this branch of chemistry.

Continue Reading16/09/2020 – AI3SD Online Seminar Series: Supramolecular Antimicrobials – the next target for AI/Machine Learning? – Dr Jennifer Hiscock

09/09/2020 – AI3SD Online Seminar Series: Using Artificial Intelligence to Optimise Small-Molecule Drug Design – Dr Nathan Brown

  • Post author:
  • Post category:

he concept of in silico molecular design goes back decades and has a long history of published approaches using many different algorithms and models. Major challenges involved in de novo molecular design are manifold, including identifying appropriate molecular representations for optimisation, scoring designed molecules against multiple modelled endpoints, and objectively quantifying synthetic feasibility of the designed structures. Recently, multiobjective de novo design, more recently referred to as generative chemistry, has had a resurgence of interest. This renaissance has highlighted a step-change in successful applications of such methods. This presentation will review the development of de novo design methods over the years including the author’s original work in this area from the early 2000s, to recent approaches that show great promise. Through this review, improvements in important components of de novo design, including machine learning model predictions and automated synthesis planning, will also be presented.

Continue Reading09/09/2020 – AI3SD Online Seminar Series: Using Artificial Intelligence to Optimise Small-Molecule Drug Design – Dr Nathan Brown

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

  • Post author:
  • Post category:

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

26/08/2020 – AI3SD Online Seminar Series: Smart Cleaning & COVID-19 – Dr Nicholas Watson

https://www.youtube.com/watch?v=o3TSkGgHI78&ab_channel=AI4ScientificDiscovery Abstract: Industrial Digital Technologies (IDTs) such as robotics, AI and IoT are transforming manufacturing worldwide with significant productivity, efficiency and environmental sustainability benefits. This digital revolution is often labelled…

Continue Reading26/08/2020 – AI3SD Online Seminar Series: Smart Cleaning & COVID-19 – Dr Nicholas Watson

26/08/2020 – AI3SD Online Seminar Series: Smart Cleaning & COVID-19 – Dr Nicholas Watson

  • Post author:
  • Post category:

Industrial Digital Technologies (IDTs) such as robotics, AI and IoT are transforming manufacturing worldwide with significant productivity, efficiency and environmental sustainability benefits. This digital revolution is often labelled Industry 4.0 and at its heart is the enhanced collection and use of data. The food and drink sector has been slow to adopt IDT’s for a variety of reasons including the availability of cost effective sensing technologies, capable of operating in production environments. This presentation will discuss the use of IDTs within the important task of food factory cleaning. It will cover the benefits and challenges of deploying robots, sensors and machine learning technologies for factory cleaning tasks in addition to the ever growing importance of effective factory cleaning during a global pandemic.

Continue Reading26/08/2020 – AI3SD Online Seminar Series: Smart Cleaning & COVID-19 – Dr Nicholas Watson

05/08/2020 – AI3SD Online Seminar Series: Dimensionality in chemistry: using multidimensional data for machine learning – Dr Ella Gale

https://www.youtube.com/watch?v=NhR7xWlAO4g&ab_channel=AI4ScientificDiscovery Abstract: In the last hundred years mankind has fully absorbed the idea of multi-dimensional space, starting with 4D space time. Due to the increase in computational power, scientists can…

Continue Reading05/08/2020 – AI3SD Online Seminar Series: Dimensionality in chemistry: using multidimensional data for machine learning – Dr Ella Gale

05/08/2020 – AI3SD Online Seminar Series: Dimensionality in chemistry: using multidimensional data for machine learning – Dr Ella Gale

  • Post author:
  • Post category:

In the last hundred years mankind has fully absorbed the idea of multi-dimensional space, starting with 4D space time. Due to the increase in computational power, scientists can now manipulate molecules in 4D (3D vibrating molecules in VR) and work with multidimensional datasets, which are needed to utilize big data and machine learning. However, our intuition from 3D space can fall down when dealing with higher dimensions and a lack of intuition can lead to mistakes in analysis. In this talk I will discuss how to think about the best dimensional space to use to describe chemical problems, how multi-dimensional space is different, techniques for using it and analysing the outputs of machine learning.

Continue Reading05/08/2020 – AI3SD Online Seminar Series: Dimensionality in chemistry: using multidimensional data for machine learning – Dr Ella Gale

07/07/2020 – AI4Good @ WebSci20

  • Post author:
  • Post category:

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.

Continue Reading07/07/2020 – AI4Good @ WebSci20