Category Archives: Small Molecules

Predicting ADME Properties with Machine Learning: 82% of the Performance From Two Descriptors

A Nature survey of ~1500 scientists reported that more than 70% had failed to reproduce another scientist’s experiments, and 50% had failed to replicate their own (Baker, 2016). This study wasn’t specific to machine learning, but the crisis has its own flavour in computational drug discovery.

One aspect of this is that available datasets have known quality issues, and results built on them can be fragile. MoleculeNet and the Therapeutic Data Commons (TDC) opened drug discovery to a wider ML community, but they’re no longer sufficient for driving further advances (Wognum et al., 2024).

Amongst a number of issues, 71% of molecules in one MoleculeNet dataset (BACE) contained at least one undefined stereocenter (a point where the same atoms can sit in two different 3D arrangements), making it unclear what chemical entity is actually being modelled (Li et al., 2026).

The stakes are high. In 2017, a research group found their cancer-target inhibitor inactive from one vendor and highly active from another. Eventually they traced this to vendors selling different mixtures of the compound’s two 3D forms where only one was active on the target and mechanism they were investigating (Baker, 2017).

Most approved pharmaceuticals are relatively small chemical molecules, typically weighing under 900 g/mol, with most of the rest being biologics. Whilst in Oxford on the UNIQ+ summer internship programme, I focused on ADME, an aspect of early stage drug discovery, where assays are measured in vitro as a stand-in to predict how a compound will behave in vivo (in the human body) before clinical trials. Absorption (does it enter the body), Distribution (where does it go), Metabolism (how quickly it’s transformed) and Excretion (how quickly it’s eliminated) are processes critical to whether a candidate succeeds. 

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The cost of a better pose: balancing GNINA sampling and runtime

If you have ever set up a docking experiment or tuned a docking workflow for GNINA, you may have found yourself asking:

What are the “best” settings for running GNINA?

Unfortunately, there is no single objectively correct answer. The optimal settings will depend on the input data, the goal of the docking experiment, and the computational resources available. One reasonable approach is simply to use the standard GNINA settings, dock each molecule once using a single conformer, and leave it at that. The default settings already perform well in many cases. But that does not mean performance cannot be improved!

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Beyond the Vaccine: AI’s Expanding Role in LNP mediated Drug Delivery

Lipid nanoparticles (LNPs) have evolved from a specialised drug-delivery technology into a cornerstone of modern medicine. Their success became evident during the COVID-19 pandemic, enabling the delivery of the mRNA used in the Pfizer-BioNTech and Moderna vaccines. However, their applications extend far beyond vaccination, as LNPs can also deliver siRNA, plasmid DNA and gene-editing machinery, creating opportunities to treat genetic diseases, cancer and many other conditions.

Yet a fundamental challenge remains: how do we design an LNP that delivers the right payload, to the right cells, in the right place?

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A first for PROTACs

Last week marked a major milestone in small-molecule drug discovery with the first FDA approval of a proteolysis targeting chimera (PROTAC). After a modest but successful phase 3 clinical trial demonstrated a 2.9 month improvement in median progression free survival1 for a type of advanced breast cancer1, the FDA has approved Veppanu (vepdegestrant), co-developed by Arvinas and Pfizer, as the first PROTAC protein degrader therapy2. So what is a PROTAC?

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SigmaDock: untwisting molecular docking with fragment-based SE(3) diffusion

Alvaro Prat, Leo Zhang, Charlotte Deane, Yee Whye Teh, & Garrett M. Morris
International Conference On Learning Representations (ICLR 2026)

Molecular docking sits at the heart of structure-based drug discovery. If we can reliably predict how a small molecule binds in a protein pocket, we can prioritize compounds faster, reason about interactions more clearly, and build better pipelines for hit discovery and lead optimization. But in practice, docking is still a difficult problem: classical methods are often robust but imperfect, while recent deep learning approaches have sometimes looked promising on headline metrics without consistently producing chemically plausible poses.

SigmaDock was built to address exactly that gap. Instead of treating docking as a problem of directly diffusing on torsion angles or unconstrained atomic coordinates, SigmaDock represents ligands as collections of rigid fragments and learns how to reassemble them inside the binding pocket using diffusion on SE(3)\text{SE}(3). In plain English: rather than trying to “wiggle” every flexible degree of freedom in a tangled way, SigmaDock breaks the ligand into chemically meaningful rigid pieces and learns where those pieces should go, and how they should reorient, to recover a valid bound pose.

Figure 1: Illustration of SigmaDock using PDB 1V4S and ligand MRK. We create an initial conformation of a query ligand where we define our mm rigid body fragments (colour coded). The corresponding forward diffusion process operates in SE(3)m\text{SE}(3)^m via independent roto-translations.
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Fragment-to-Lead Successes in 2024 – 10th Anniversary Edition

In what I have to admit is now becoming an annual tradition ([2023] [2019]), I’d like to highlight the 2024 edition of the fragment-to-lead success stories, published in J. Med. Chem. at the end of 2025 [Paper].

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New DPhil/PhD Programme in Pharmaceutical Science Joint with GSK!

Many OPIGlets found their way into a DPhil in Protein Informatics through our Systems Approaches to Biomedical Sciences Industrial Doctoral Landscape Award, which was open to applicants 2009-2024. This innovative course, based at the MPLS Doctoral Training Centre (DTC), offered six months of intensive taught modules prior to starting PhD-level research, allowing students to upskill across a diverse range of subjects (coding, mathematics, structural biology, etc.) and to go on to do research in areas significantly distinct from their formal Undergraduate training. All projects also benefited from direct co-supervision from researchers working in the Pharmaceutical industry, ensuring DPhil projects in areas with drug discovery translation potential. Regrettably, having twice successfully applied for renewal of funding, we were unsuccessful in our bid to refund SABS in 2024.

Happily though, we can now formally announce that our bid for a direct successor to SABS, the Transformative Technologies in Pharmaceutical Sciences IDLA, has been backed by the BBSRC, and we will shortly be opening for applications for entry this October [2026]. As someone who benefited from the interdisciplinary training and industry-adjacency of SABS, I’m thrilled to be a co-director of this new Programme and to help deliver this course to a new generation of talented students.

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What Molecular ML Can Learn from the Vision Community’s Representation Revolution

Something remarkable happened in computer vision in 2025: the fields of generative modeling and representation learning, which had developed largely independently, suddenly converged. Diffusion models started leveraging pretrained vision encoders like DINOv2 to dramatically accelerate training. Researchers discovered that aligning generative models to pretrained representations doesn’t just speed things up—it often produces better results.

As someone who works on generative models for (among other things) molecules and proteins, I’ve been watching this unfold with great interest. Could we do the same thing for molecular ML? We now have foundation models like MACE that learn powerful atomic representations. Could aligning molecular generative models to these representations provide similar benefits?

In this post, I’ll summarize what happened in vision (organized into four “phases”), and then discuss what I think are the key lessons for molecular machine learning. The punchline: many of these ideas are already starting to appear in our field, but we’re still in the early stages compared to vision.

For a more detailed treatment of the vision developments with full references and figures, see the extended blog post on my website.

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Chemical Languages in Machine Learning

For more than a century, chemists have been trying to squeeze the beautifully messy, quantum-smeared reality of molecules into tidy digital boxes, “formats” such as line notations, connection tables, coordinate files, or even the vaguely hieroglyphic Wiswesser Line Notation. These formats weren’t designed for machine learning; some weren’t even designed for computers. And yet, they’ve become the wedged into the backbones of modern drug discovery, materials design and computational chemistry.

The emergent use of large language models and natural language processing in chemistry posits the immediate question: What does it mean for a molecule to have a “language,” and how should machines speak it?

if molecules are akin to words and sentences, what alphabet and grammatical rules should they follow?

What follows is a tour through the evolving world of chemical languages, why we use them, why our old representations keep breaking our shiny new models, and what might replace them.

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Controlling the Diffusion Denoising Process: A Molecular Show

This blog post is supporting my poster at Young Modellers Forum and makes things way easier to see and understand. Underneath each GIF, is the explanation of what you should look for as things denoise throughout the diffusion trajectory. Click the GIFs for higher quality viewing!

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