Archived Newsletters

UK-QSAR Autumn 2024

The UK-QSAR and Cheminformatics Group

Welcome to the Autumn 2024 UK-QSAR Newsletter!

 

Exscientia looks forward to welcoming you to the Autumn 2024 UK-QSAR meeting, to be held on 17th October 2024 at Lady Margaret Hall, Oxford.

As always, the event is free of charge to attend but registration in advance is essential – “walk-up” attendance is not possible. However it will be held in person and will not be streamed online or recorded for later access.

The registration is now closed.

The themes of the meeting will be AI, ML and computational toxicology.

Please note that paper copies of the agenda and abstracts will not be provided onsite, so we recommend that you bookmark this page on your device or screenshot the agenda section.

Our newsletter contains more details of the meeting  below including the draft Agenda,  travel and accommodation information, info for  poster presenters and  talk abstracts/pre-reading material, as well as the usual sections on  job openings and upcoming  meetings which may be of interest.  We also have information on our new UK-QSAR Discord server.  To encourage use of this, if you’d like to join the pre-meeting dinner in Oxford in October, you’ll need to do this via the Discord server!  A separate email with information on how to join the pre-meeting meal will be sent separately.

Sadly, another esteemed member of our community is no longer with us.  We send warm thoughts and condolences to the family, friends and associates of Mohammad Afshar, latterly of Ariana Pharmaceuticals.  A tribute to Mohammad is below.

Our Spring Meeting 2025  will be held at the Crick Institute in London, and will be sponsored by OpenEye/Cadence and MSD.  More details will follow on our website.

Autumn Meeting Information

REGISTRATION

Registration is now closed.

AGENDA

The provisional agenda is:

 

09:00-10:00	Open registration, coffee/tea
10:00-10:15	Welcome and opening remarks
	Session 1: Generative AI in drug discovery
10:15-10:45	Morgan Thomas (Universitat Pompeu Fabra) – “Structure-based generative molecular design: from 2D or 3D?”
10:45-11:15	José Jiménez-Luna (Microsoft Research) – “Learning chemical intuition from chemist on the loop”
11:15-11:45	Finton Sirockin (Novartis) – “Generative Chemistry at Novartis: a view from the trenches”
11:45-12:05	Marcel Verdonk and Noj Malcolm (Astex Pharmaceuticals & Schrodinger) – “Richard Hall: A reflection on a life cut short”
12:05-13:30	Lunch and poster session
	Session 2: Machine Learning in drug discovery
13:30-14:00	Liam Wilbraham (Exscientia) – “Active learning on synthons for molecular design”
14:00-14:30	Lindsay Willmore (Google DeepMind) – “AlphaFold 3: What’s new and improved and what’s left to do”
14:30-15:00	Break and poster session
	Session 3: Computational toxicology
15:00-15:30	Vigneshwari Subramanian (AstraZeneca) – “Predicting safety liabilities of small molecules with machine learning : An industry perspective”
15:30-16:00	Layla Hosseini-Gerami (Ignota Labs) – “Harnessing AI for Mechanistic Insights into Drug Failures: A Path to Salvageability”
16:00-16:30	Daniella Hares (Institute of CancerResearch) – “Enhancing Small Molecule Binding through Computational Analysis of Water Networks” Poster winner of the Spring 2024 event
16:30-17:00	Concluding remarks, poster prize winner announcement and close

LOCATION AND TRAVEL

LMH is committed to sustainable travel and encourages the use of energy efficient public and shared transport, bicycles and walking for visitors travelling to the College. By discouraging unnecessary travel and the use of private motor transport, the aim is to reduce carbon emissions and reduce traffic and congestion in the Oxford area. The College is located to the north of the city centre, adjacent to the University Parks:  Norham Gardens, Oxford, OX2 6QA

A comprehensive guide to travel to LMH can be found here.

There is on-street parking available on the roads surrounding LMH, including Fyfield Road, Norham Road, Crick Road, Norham Gardens and Bradmore Road. Please take great care to check the pay and display terminals for information when you park. Do not rely solely on the road signs with timings, as some are incorrect. For parking costs, please visit the Oxfordshire County Council website. Priority parking in the College car park on Fyfield Road is given to those with disabilities.

HOTELS

There are numerous hotels in central Oxford.  The Mercure and Premier Inn are centrally located, whilst the Travelodge and  Holiday Inn are more towards the city outskirts.  A useful list of other hotels to consider can be found here.

For a more authentic Oxford college experience, University Rooms  may have availability in college accommodation.

FOR POSTER PRESENTERS

Abstract submission is now closed.

Please follow these links to download the posters list and abstracts.

UK-QSAR Discord Server

Nathan Brown, Healx, aided and abetted by ChatGPT

What is a Discord Server?

A Discord server is an online space where communities can gather, communicate, and share information. It is like a virtual club with different “channels” (chat rooms) for specific topics. Discord servers allow members to chat via text, voice, or video and can support a wide range of activities such as sharing files, hosting discussions, and collaborating on projects.

Why is a Discord Server Helpful for the UKQSAR ?

For a scientific society, a Discord server can be an incredibly valuable tool for fostering collaboration, communication, and networking. Some benefits include:

• Real-Time Collaboration: Members can exchange ideas quickly and work together on research projects, workshops, or events.
• Networking Opportunities: Members can easily interact with peers, mentors, and experts in their field.
• Resource Sharing: Members can share papers, datasets, tools, and other scientific resources.
• Special Interest Groups: Channels can be created for specific areas of research or expertise, encouraging focused discussions and activities.

We will also be using the server in helping with the organisation of our meetings, including those attending to self-organise for events before and after the meetings such as joining for dinners and drinks.

How to Join and Contribute to a Discord Server

• Create a Discord Account (if you don’t already have one):
• • Go to discord.com and click on Sign Up.
• • Enter your email, username, password, and date of birth, then follow the prompts to create your account.
• Join the Server:
• • Once you receive an invite link to the scientific society’s Discord server, click on the link.
• • If you are logged into Discord, you will be automatically added to the server. If not, log in first.
• • You will be prompted to agree to the server’s rules before joining.
• Familiarize Yourself with the Server Layout:
• • On the left-hand side, you’ll see a list of channels (chat rooms) organized by topics
• • Channels may be categorized into sections, such as:
• • General Discussions: For broad conversations.
• • Research Channels**: For discussing specific scientific topics.
• • Events: For announcements about upcoming events, workshops, or meetings.
• • Some servers may also have voice channels for live discussions or video meetings.
• Introduce Yourself:
• • Look for the introductions channel. Introduce yourself by briefly stating your name, field of research, and what you hope to contribute or gain from the community.
• Contribute to Discussions:
• • Engage in ongoing conversations by responding to messages, asking questions, or providing insight into scientific discussions.
• • Share research papers, articles, or helpful tools in designated resource-sharing channels.
• • Respectfully participate in debates and support others’ contributions by offering constructive feedback.
• Join or Lead Initiatives:
• • Many Discord servers have opportunities for members to start or join projects, such as collaborative research, writing papers, or organizing events.
• • Look for announcements or calls to action and get involved in those that align with your expertise or interests.
• • If you have an idea for an initiative, suggest it in the appropriate channel.
• Follow Server Guidelines:
• • Each server has its own set of guidelines or rules to maintain a positive and productive environment. These rules are found in a channel titled welcome-and-rules. Make sure to read and adhere to them.
• • Be respectful and professional in your interactions.
• Stay Active:
  • Consistent participation is key to making the most out of the server. Regularly check for updates, participate in discussions, and attend any live events or meetings.

By following these steps, you can effectively join, contribute, and benefit from a Discord server set up for a scientific society. You’ll have the chance to connect with peers, share knowledge, and collaborate on research projects, all while helping build a strong, active community.

Mohammad Afshar

It is with profound sadness that we salute the passing of another true pioneer of our science, Mohammad Afshar.  Ariana Pharma, which he founded, have posted a fitting tribute to him on LinkedIn which we would like to bring to the attention of our readers.

Abstracts & Pre-Reading Material

Some speakers have provided the following abstracts and references which might be of interest ahead of their talks.

Structure-based generative molecular design: from 2D or 3D?
Morgan Thomas, Universitat Pompeu Fabra

Several approaches can be taken on the path to the automated design of small molecules as candidate drugs that bind to therapeutically relevant protein structures. ML-based generative molecular design approaches can be mostly reduced to structure-implicit or structure-explicit, i.e., molecule building in 2D or in 3D. Recent comparisons between these approaches have highlighted limitations with regards to protein-ligand interactions and ligand strain. In this work this comparison has been extended to also compare the quality of chemistry proposed, revealing that structure-implicit generative design from 2D remains dominant. This re-iterates the challenges that 3D models still face including training data quantity, quality and required aspects such as novelty. Furthermore, this poses questions with regards to research direction in the field, and the quality of evaluations currently published. As an example of successful structure-based generative design from 3D, we have used reinforcement learning and molecular docking utilising domain expertise to identify novel hits against a GPCR target.

Learning chemical intuition from chemist on the loop
José Jiménez-Luna, Microsoft Research

The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very time-consuming process that typically spans many years within a chemist’s career. In this talk I’ll show how we replicated this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis over the course of several months. I’ll also exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization, motif rationalization, and biased de novo drug design.

Generative Chemistry at Novartis: a view from the trenches
Finton Sirockin, Novartis

This presentation will discuss the integration of Generative Chemistry (GenChem) methods into medicinal chemistry projects at Novartis. Specifically, it will delve into the lessons learned over the past years. The presentation will emphasise the practical aspects of integrating GenChem methods into the day-to-day ideation and prioritisation processes of medicinal chemistry teams.

Richard Hall: A reflection on a life cut short
Marcel Verdonk and Noj Malcolm, Astex Pharmaceuticals & Schrodinger

Personal and professional tribute to Richard Hall, who died 15th December 2023.

Active learning on synthons for molecular design
Liam Wilbraham, Exscientia

Generative design and library enumeration are two important compound generation strategies in computational small molecule drug discovery. The former aims to refine small molecule structures to align with predefined multi-parameter objectives while maintaining sample efficiency but can lead to compounds that are challenging to synthesize due to the absence of explicit chemistry constraints. Library enumeration strategies apply common medicinal chemistry reactions to commercially-available building blocks, creating a synthetically-tractable chemical space. However, as this method is frequently used to generate compounds combinatorially, scoring the enumerated library beyond targeted enumeration schemes can become computationally intractable. To achieve the sample efficiency enjoyed by generative approaches while maintaining the synthetic tractability offered by library enumeration, we introduce Synthon Active Learning (SAL). SAL bridges both methods via a factored active-learning approach, building models to rank synthons according to a multi-parameter objective, the best of which are exhaustively enumerated to create high-scoring, synthesizable molecules. We demonstrate SAL’s efficiency experimentally: by evaluating just 6% of a space containing 1M molecules, we recover 82% and 100% of the top 100 molecules for a docking and 3D similarity objective, respectively. Further, we show our method scales beyond the domain of exhaustive screening, finding increasingly high-scoring molecules in spaces of up to 10^12 molecules. Finally, we validate SAL for molecule design tasks on three distinct protein targets using ligand-based, structure-based, and drug-likeness scoring functions. We show that SAL-generated molecules obtain comparable or better ADMET, synthesizability, and chemical property distributions compared to known bioactive compounds while successfully optimising for the target objective.

AlphaFold 3: What’s new and improved and what’s left to do
Lindsay Willmore, Google DeepMind

AlphaFold 3 represents a significant advancement in the field of structural biology, enabling the simultaneous prediction of complex structures involving diverse biological components such as proteins, nucleic acids, small molecules, ions, and modified residues. Furthermore, the model has improved accuracy in predicting the structures of protein-ligand and protein-nucleic acid interactions compared to specialized tools. I will take you through the model updates and training schemes used to achieve this enhanced model coverage and accuracy. And I will also discuss some of the unexpected wins and shortcomings of the new AlphaFold model.

Predicting safety liabilities of small molecules with machine learning: An industry perspective
Vigneshwari Subramanian, AstraZeneca, Gothenburg, Sweden

Ensuring right safety has been a part of AstraZeneca’s 5R framework for the past decade and
adoption of this framework has had a significant impact on improving our project success rates
and reducing drug attrition. Screening cascades involving in vitro and in vivo assays have been
developed to assess safety liabilities, however, in silico approaches have also been exploited
extensively to support decision-making in projects. Having the potential to predict safety-related
end points early on would not only prevent compounds with safety liabilities progressing further,
but also support the design of molecules with improved safety profiles. This talk will primarily
focus on our machine learning modeling efforts to support safety and organ-level toxicity
assessments, including predicting off-target pharmacology, phospholipidosis, Drug Induced Liver
Injury and QTc prolongation (a potential indicator of cardiac toxicity).

Harnessing AI for Mechanistic Insights into Drug Failures: A Path to Salvageability
Layla Hosseini-Gerami, Ignota Labs

The pharmaceutical industry witnesses the attrition of over a thousand drug candidates annually during pre-clinical or Phase I studies, due to safety concerns. The limited mechanistic understanding of these failures poses a significant hurdle in diagnosing root causes and identifying potential avenues for salvaging these compounds. This talk will explore the innovative approach developed by Ignota Labs, which leverages artificial intelligence to transform the landscape of drug development by providing mechanistic insights into drug failures. Ignota Labs employs a causal knowledge graph methodology, integrating multimodal data sources to create a comprehensive, granular understanding of drug actions and their associated adverse effects. This  approach enables the generation of detailed hypotheses about the mechanisms underpinning observed safety issues. By systematically analysing data from various biological, chemical, and clinical domains, the AI-driven platform can identify critical causal events and suggest modifications to improve the safety profile of drug candidates. The presentation will delve into the technical aspects of the causal knowledge graph, highlighting how we process and integrate diverse datasets to construct a detailed mechanistic map. A case study will be presented to illustrate the practical application of this methodology, demonstrating how our approach can diagnose specific mechanistic failures and propose actionable strategies for drug redesign. By elucidating the root causes of drug failures, this AI-based approach not only enhances the understanding of drug safety but also opens new pathways for rescuing potentially valuable therapeutics. This talk aims to inspire new thinking and innovation in drug development, giving a glimpse into a future where AI-driven insights significantly reduce attrition rates by offering a second chance to promising drug candidates.

Enhancing Small Molecule Binding through Computational Analysis of Water Networks
Daniella Hares, Institute of Cancer Research

Water networks can have a critical role in small molecules binding to a target protein and are an important consideration in structure-based drug design. Previous computational studies have predicted protein-bound water molecules to contribute between -3 to -6 kcal mol-1 towards binding affinity [1]. When modifying the molecular structure of a lead compound, the rearrangement of surrounding water networks in the binding site can impact potency but this contribution is challenging to measure experimentally. Computational methods are ideally suited to study the interplay between ligand optimisation and water displacement by predicting the effect of structural changes on both the activity of the compound and the stability of neighbouring water molecules. We used Grand Canonical Monte Carlo simulations [2] and alchemical free energy calculations to retrospectively rationalise the trends in potency observed in a set of B-cell Lymphoma 6 (BCL6) inhibitors [3]. As part of the drug design process, the inhibitor molecular structure was modified to displace water molecules that were part of a network within the protein binding site. Using the BCL6 project as an example, this talk will demonstrate how  computational approaches can shine a light into an aspect that is often overlooked but essential to guide the design of better compounds when working with hydrated protein pockets. We show the power of these methods and encourage their use more widely, particularly in prospective applications on drug discovery projects.

[1] Mobley, D. L. and Dill K. A. (2009). Structure, 17 (4), 489–498.
[2] Ross, G. A., Bodnarchuk, M. S. and Essex, J. W. (2015). J. Am. Chem. Soc., 137 (47), 14930–14943.
[3] Lloyd, M. G., Huckvale, R., Cheung, K.-M. J., Rodrigues, M. J., Collie, G. W., Pierrat, O. A., Gatti Iou, M., Carter, M., Davis, O. A., McAndrew, P. C., Gunnell, E., Le Bihan, Y.-V., Talbot, R., Henley, A. T., Johnson, L. D., Hayes, A., Bright, M. D., Raynaud, F. I., Meniconi, M., Burke, R., van Montfort, R. L. M., Rossanese, O. W., Bellenie, B. R., Hoelder, S. (2021). J. Med. Chem., 64 (23), 17079–17097.

Jobs

• Computational Chemists, Isomorphic Labs, London https://boards.greenhouse.io/isomorphiclabs/jobs/5223454004
• Cheminformatics Scientist, GSK, Stevenage, UK https://gsk.wd5.myworkdayjobs.com/GSKCareers/job/UK—Hertfordshire—Stevenage/Cheminformatics-Scientist_403883-1

Upcoming Meetings

Nessa Carson of AZ has been compiling a comprehensive list of scientific conferences, which can be found here.

Notable conferences of particular interest to our readers include:

• EuroQSAR, Barcelona, Spain, 22nd September 2024
• ELRIG Drug Discovery 2024, London, 2-3 Oct 2024
• Computational Drug Design – A tribute to Frank Blaney, GSK Stevenage, 4 Oct 2024
• MGMS Young Modellers’ Forum, LMH Oxford, 29th Nov 2024
• UKQSAR Spring 2025 Meeting,  Crick Institute London; details will be posted to https://ukqsar.org/index.php/category/meetings/ when available