Sheffield 9th Conference on Cheminformatics 

The Sheffield Conference on Cheminformatics is always one of the highlights of the calendar, it will be held at The Edge, University of Sheffield, UK, Monday 19th – Wednesday 21st June, 2023.

As usual a great lineup of speakers

Confirmed Attendees & Titles of Paper:

  • Adele Hardie A World of Probabilities: An sMD/MSM Approach for Rational Design of Allosteric Modulators
  • Aras Asaad Persistence homological statistical summaries for ligand-based virtual screening
  • Benoit Baillif Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
  • Dan Woodward Coverage Score: A Model Agnostic Method to Efficiently Explore Chemical Space
  • David Palmer Simultaneous Entropy, Enthalpy and Free Energy Prediction using a Physics-Informed Neural Network and Multi-task Learning
  • Lauren Reid SARkush®: Automated Markush-like structure generation using matched pairs and generic atom scaffolds
  • Helle van den Maagdenberg QSPRpred: a Flexible and Open Quantitative Structure-Property Relationship Modelling Tool
  • Henriette Willems PI5P4K subtype-selective inhibitors: three binding modes from one privileged motif
  • James Webster An in-silico benchmarking platform for generative de novo drug design
  • Marc Lehner Partial Charge Prediction and Pattern Extraction from a AttentiveFP Graph Neural Network
  • Maria J Falaguera Illuminating the Chemical Space of Untargeted Proteins
  • Matteo Ferla Fragmenstein: stitching compounds together
  • Maximilian Beckers Prediction of small molecule developability using large-scale in silico ADMET models
  • Moritz Walter Integrating heterogeneous assay data for ML-based ADME prediction
  • Noel O’Boyle Handling large chemical spaces in Structure-Based Drug Design
  • Rajarshi Guha Virtual Screening of Virtual Libraries using a Genetic Algorithm
  • Richard Gowers The Open Free Energy Consortium: Alchemistry for everyone
  • Richard Sherhod Glolloc: a global-local mixture of experts model and its application to small molecule drug discovery
  • Roger Sayle FNGRPRNTS: Processing just the bits you need, and none of the 1s you don’t.
  • Roxana-Maria Rujan Resolving code names to structures from the medicinal chemistry literature: not as FAIR as it should be
  • Samuel Genheden AiZynthFinder: developments and learnings from three years of industrial application
  • Sébastien Guesné Beyond balanced accuracy: balanced Matthews’ correlation coefficient.
  • Sohvi Luukkonen DrugEx: deep learning for de novo drug design — a case for A2B selective ligands
  • Srijit Seal PKSmart: An Open-Source Computational Model to Predict in vivo Pharmacokinetics of Small Molecules
  • Tuomo Kalliokoski Efficient structure-based virtual screening of ultra-large enumerated chemical spaces using macHine leArning booSTEd dockiNg (HASTEN)
  • Uschi Dolfus Full modification control over retrosynthetic routes for guided optimization of lead structures

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