A5069319023- Computational Drug Discovery Methods
- Chemical Synthesis and Analysis
- Microbial Natural Products and Biosynthesis
- Protein Degradation and Inhibitors
- Glycosylation and Glycoproteins Research
- Ion-surface interactions and analysis
- Machine Learning in Materials Science
- Machine Learning in Bioinformatics
- X-ray Diffraction in Crystallography
- Elasticity and Material Modeling
- Crystallization and Solubility Studies
- Seismic Imaging and Inversion Techniques
- Ubiquitin and proteasome pathways
- Nuclear Materials and Properties
- Viral Infectious Diseases and Gene Expression in Insects
- Advanced biosensing and bioanalysis techniques
- vaccines and immunoinformatics approaches
- Protein Structure and Dynamics
- Peptidase Inhibition and Analysis
- Inorganic Chemistry and Materials
- Click Chemistry and Applications
University of Oxford
2019-2025
Diamond Light Source
2023-2024
Genomics (United Kingdom)
2022-2023
Over the past few years, many machine learning-based scoring functions for predicting binding of small molecules to proteins have been developed. Their objective is approximate distribution which takes two as input and outputs energy their interaction. Only a function that accounts interatomic interactions involved in can accurately predict affinity on unseen molecules. However, make predictions based data set biases rather than an understanding physics binding. These perform well when...
Fragment approaches are long‐established in target‐based ligand discovery yet their full transformative potential lies dormant, because progressing hits to potency remains underserved by methodological work. The only credible progression paradigm is multiple cycles of costly conventional design‐make‐test‐analyse (DMTA) medicinal chemistry, necessitating picking winners early and discarding others. It effective cheaply parallelize large numbers non‐uniform multi‐step reactions, because, even...
Fragment approaches are long‐established in target‐based ligand discovery yet their full transformative potential lies dormant, because progressing hits to potency remains underserved by methodological work. The only credible progression paradigm is multiple cycles of costly conventional design‐make‐test‐analyse (DMTA) medicinal chemistry, necessitating picking winners early and discarding others. It effective cheaply parallelize large numbers non‐uniform multi‐step reactions, because, even...
Abstract A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess predictive power of methods involved computer-aided drug design. Application various to molecules are now widely search new drugs. However, there is little way systematic validation specifically fragment-based approaches. We have performed a large high-throughput screen against therapeutically relevant second bromodomain...
Fragment approaches are long-established in target-based ligand discovery. Nevertheless, their full transformative potential lies dormant, because progressing hits to potency remains difficult and underserved by methodology developments, which mostly focus on screening. The only credible progression paradigm is conventional design-make-test analyse (DMTA) medicinal chemistry, costly thus necessitates picking winners early, thereby effectively discarding all the other hits. We here...
We demonstrate that a simple workflow of array synthesis, combining low-cost robotics with analytic techniques to deconvolute crude reaction mixtures, is an effective way collect structural data on binding site. Starting from the high information content crystallographic fragment screens PHIP(2) (second bromodomain pleckstrin homology domain interacting protein), collection more than 1800 compounds was enumerated. Several thousand Crude Reaction Mixtures (CRMs) were synthesized one robotic...
ABSTRACT Over the last few years, many machine learning-based scoring functions for predicting binding of small molecules to proteins have been developed. Their objective is approximate distribution which takes two as input and outputs energy their interaction. Only a function that accounts interatomic interactions involved in can accurately predict affinity on unseen molecules. However, make predictions based dataset biases rather than an understanding physics binding. These perform well...