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Overview

This Royal Society conference will bring together stakeholders from industry and academia to explore advances in machine learning and artificial intelligence for biological research, drug discovery and medicine. Whilst these computational technologies are already transforming biological, clinical and pharmaceutical research, significant obstacles remain, in particular around skills, financing, and accessibility. Concerted industry/academia collaboration will be required to address these challenges.

Following a keynote address from Dr Demis Hassabis CBE FREng FRS (DeepMind), three sessions around biology, chemistry and medicine will include talks on machine learning for target discovery and 'omic technologies, AI-based chemoinformatics, and computational clinical trial design, as well as the classical prediction of protein folding.

The conference will conclude with a panel discussion to address how to reintegrate data from specialised biological subdisciplines, reflect on the barriers faced by researchers when accessing and using machine learning technologies, and consider strategies to drive continued interdisciplinary innovation.

About the conference series

Supported by AstraZeneca, the meeting forms part of the Royal Society’s Transforming our future series in the life sciences. These meetings are unique, high-level events that address the scientific and technical challenges of the next decade. Each conference features cutting-edge science from industry and academia and brings together leading experts from the scientific community, including regulatory, charity and funding bodies.

Attending this event

  • This will be a hybrid event
  • In-person attendance is invitation only
  • Please register to attend online
  • For the programme, scroll down and click on the arrows to see the speakers and abstracts for their talks
  • The Royal Society has an acceptable use policy for all online events, and we expect our users to abide by these guidelines
  • For any queries or accessibility requirements, please email the Industry Team.

Organisers

Schedule

09:00-09:05
Opening remarks

Speakers

Sir Adrian Smith

President, the Royal Society


Chair

Dr Claus Bendtsen

AstraZeneca

09:10-09:30
Using AI and ML for target discovery

Speakers

Dr Anne Phelan

BenevolentAI, UK

09:30-09:50
Mapping and navigating biology and chemistry with genome-scale imaging

Abstract

Image-based readouts of biology are information-rich and inexpensive. Yet historically, bespoke data collection methods and the intrinsically unstructured nature of image data have made these assays difficult to work with at scale.

This presentation will discuss advances made at Recursion to industrialise the use of cellular imaging to drive drug discovery. In particular, the use of deep learning allows the transformation of unstructured images into biologically meaningful representations, and enables a 'map of biology' relating genetic and chemical perturbations to scale drug discovery. Dr Haque will further discuss how publicly-shared resources from Recursion, including the RxRx3 dataset and MolRecTM application, enable downstream research both on cellular images themselves and on deep learning-derived embeddings, making advanced image analysis more accessible to researchers worldwide.

Speakers

Dr Imran Haque

Recursion Pharmaceuticals

09:50-10:10
Digital Twins for personalised oncology

Abstract

Cancer incidence is steadily increasing and is a major burden for patients, their families and society. Despite new treatments such as immunotherapies, currently only around 25% of cancer patients respond to treatments with drugs or biologicals leading to a cure or a decrease in disease progression (Spear et al 2001). New approaches are needed.

In this talk, Walter will introduce Digital Twin strategies that aim to improve cancer diagnosis and treatment by constructing computer models of cancer patients. These models allow simulation of the disease in silico and optimisation and selection of the best possible therapy for each patient. Using childhood cancer as a paradigm, Walter will discuss the status of Digital Twin technology, what the current challenges are and what he hopes the field will achieve over the next five to ten years.

Speakers

Professor Walter Kolch

University College Dublin

10:10-10:35
Q&A and discussion

Speakers

Professor Walter Kolch

University College Dublin

Dr Imran Haque

Recursion Pharmaceuticals

Dr Anne Phelan

BenevolentAI, UK


Chair

Dr Harren Jhoti OBE FMedSci FRS

Astex Pharmaceuticals

11:10-11:30
Integrating chemical and biological data: a focus on relevance and translation will boost in vivo-relevant drug discovery

Abstract

The amount of chemical and biological data has increased in both public and private domains, and algorithm and hardware design for machine learning has also progressed tremendously in the last ten years. This has enabled rapid development of machine learning for drug discovery. Several ‘AI-designed drugs’ have already entered clinical phases, and press releases now describe the design of functional proteins and antibodies from scratch.

However, the attempt to marry algorithms with drug discovery often disregards the in vivo relevance of our current capabilities for processing chemical and biological data. In this talk, Dr Bender will pose that reductionist thinking remains pervasive in the field, and how, in combination with a lack of relevant data, our limited ability to handle it computationally with respect to in vivo-relevant decisions and the formation of many narrow specialist domains, this is undermining our ability to harness the full potential of available chemical and biological data.

This talk will discuss how changing several areas, including data usage, algorithms and human mindset, might enable society to fully benefit from available computer power when it comes to in vivo-relevant decision making in drug discovery in the future.

Speakers

Professor Andreas Bender

University of Cambridge and Pangea Botanica

11:30-11:50
Machine learning to predict protein function from sequence - therapeutic applications

Abstract

A central challenge in biochemistry is the prediction of the functional properties of a protein from its amino acid sequence, as it can lead to the discovery of new proteins with specific functionality and a better understanding of the functional effect of genomic mutations. Experimental and computational data enable the training and validation of powerful machine learning models that predict protein function directly from sequence. This talk will present deep learning models that accurately predict functional domains within protein sequences, and large language models that generate textual descriptions of protein sequences, collectively adding millions of annotations to public databases.

Technical breakthroughs enable data on the sequence-to-function relationship to be rapidly acquired. However, the cost and latency of wet lab experiments means that we require new methods to find 'hits' (sequences that meet the function requirements of the campaign) in few experimental rounds, where each round contains a large batch of sequence designs.

In this talk, Dr Colwell will discuss model-based optimisation approaches that take advantage of sample inefficient methods to find diverse sequence candidates for experimental evaluation. The potential of these approaches will be illustrated through three case studies demonstrating the design and experimental validation of proteins and peptides for therapeutic applications.

Speakers

Dr Lucy Colwell

University of Cambridge, UK

11:50-12:10
Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding

Abstract

There is significant interest in developing machine learning models that can predict protein-ligand binding with high accuracy. Many recently proposed structure-based virtual screening models have done just this. However, there are still challenges in ensuring that these models are doing more than exploiting ligand-specific biases in the dataset making them potentially excellent predictors for proteins/ligands that are contained within the dataset but unable to generalise to unseen examples. 

One way to assess if a model can generalise is to test whether it understands the rules of intermolecular binding. Using synthetic data, the Deane lab has investigated whether different methods (from fingerprint-based random forests to deep learning virtual screening models) are learning more than the biases in datasets.

The Deane lab found that their deep learning based virtual screening model, PointVS, identifies important functional groups with more efficiency than other methods tested. This suggests that it may generalise more effectively to new examples.

Using attribution, the Deane lab demonstrated that PointVS can identify important interactions in real protein ligand complexes, and further, that it can be used to extract important binding pharmacophores from a given protein target when supplied with a number of bound structures. This information was then used to perform fragment elaboration, resulting in improvements in docking scores when compared to using structural information from a traditional data-based approach. 

This not only provides definitive proof that PointVS is learning to identify important binding interactions, but also constitutes the first deep learning-based method for extracting structural information from a target for molecule design. This presents an exciting opportunity for the future of lead discovery and drug development.

Speakers

Professor Charlotte Deane MBE

University of Oxford

12:10-12:35
Q&A and discussion

Speakers

Professor Andreas Bender

University of Cambridge and Pangea Botanica

Professor Charlotte Deane MBE

University of Oxford

Dr Lucy Colwell

University of Cambridge, UK


Chair

Professor Mihaela van der Schaar

University of Cambridge

13:40-14:00
Transforming the practice of medicine - a human-centred perspective

Abstract

‘Healthcare is perhaps AI’s most urgent application' – Satya Nadella, CEO of Microsoft.

This urgency has been brought to life for all by the COVID-19 pandemic; now, healthcare providers are expected to perform and transform at scale, in real time. The recent disruptions in AI have the potential to revolutionise medical care, but to fully leverage its potential, a human-centred approach is essential.

This talk will share various opportunities and challenges for AI to deliver real-world impact, while prioritising the human aspect of high-quality care. Illustrative examples will highlight how a human-centred approach is crucial in transforming the practice of medicine for the future.

Speakers

Aditya Nori

Microsoft Health Futures

14:00-14:20
Designing clinical trials using AI

Speakers

14:20-14:40
Machine learning for translatable biomarkers and targets

Abstract

Despite significant progress in modern medicine, the design and development of new medicines remains very challenging and with a low probability of success. Improving our understanding of the underlying biology of disease can help identify new therapies and predict which interventions will positively (or negatively) affect clinical outcomes in diverse groups of patients.

This talk will describe how insitro is using cutting-edge machine learning methods to develop a new approach to drug development that uses biological and clinical data to design novel, safe, and effective therapies that help more people, faster and at a lower cost. The process begins with the generation and aggregation of large amounts of high-content biomarker data from both human samples and human-derived cellular systems. These data are used to create a representation of biological states, enabling the construction of machine learning models that reveal novel therapeutic targets, identify coherent patient segments and predict the effect of different therapies on different patients. It will also explore insitro’s vision for the value of AI-driven biomarker identification in precision medicine.

Speakers

14:40-15:05
Q&A and discussion

Speakers

Aditya Nori

Microsoft Health Futures


Chair

Professor Mihaela van der Schaar

University of Cambridge

15:35-16:45
Panel discussion

Speakers

Jia-Yi Har

Cathay Capital

Prof Eoin McKinney

University of Cambridge

Dr Thomas Callender

University College London

16:45-17:25
Closing keynote

Speakers


Chair

Sir Adrian Smith

President, the Royal Society

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