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About M2D2

Welcome to this space dedicated to the M2D2 Talks co-organized by Valence Discovery and Mila - Quebec AI Institute.

From applied research papers to open source projects, we're hoping to use these talks to help demystify AI for drug discovery and make the field more accessible for newcomers. M2D2 will bring our vibrant AI & drug discovery communities together and spark new perspectives, provoke discussions, and offer a safe space to share new ideas.

A wide range of drug discovery related topics will be covered reflecting the vibrant diversity of tools and methodologies in the community:

  • Applications of ML in computational molecular design
  • Representation learning for small- and macromolecules
  • Prediction of molecular properties and bioactivities
  • ML for quantum chemistry and molecular dynamics
  • Generative models for de novo molecular design
  • Multiparameter optimization and compound selection
  • Interpretable and explainable activity models
  • Reaction prediction, retrosynthesis, and synthesis planning
Illustration with molecules


Please reach out if you would like to present at an upcoming event.

Upcoming events

July 2022
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Latest Recorded Talks

Whenever possible, slides and videos will be available after each talk.

Bayesian modelling of synergistic drug combination effects in cancer using Gaussian Processes

Bayesian modelling of synergistic drug combination effects in cancer using Gaussian Processes

High-throughput drug sensitivity experiments in cancer enable rapid in-vitro testing of various compounds on cancer cell lines, or patient-derived material, in order to determine the efficacy of a certain treatment. Accurate prediction of dose-response functions from a limited set of pre-clinical experiments is key to explore the large space of possible treatment options, or to prioritize which experiments to perform. This is particularly important when predicting the effect of drug combinations, where it is unfeasible to test all possible combinations. Drug sensitivity experiments are noisy by nature, due in part to the natural biological variability of cell growth but also technical error sources in the assays. This entails that the experimental observations of dose-response at different concentrations of a drug vary in estimation certainty both within and between experiments — a variability that has been often ignored in the literature.<br>In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). The interaction is modelled in a flexible manner, using a latent Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results.<br>We further extend the model from single-experiment to multi-experiment modelling and propose PIICM: a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian Process regression to predict dose-response surfaces in untested drug combination experiments. The permutation invariance accounts for natural symmetries in the dose-response surfaces for drug combinations, which when not accounted for can have detrimental effects on prediction performance. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, and the training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.

Leiv Rønneberg

Three open-source initiatives to get you started with AI in drug discovery

Hadrien Mary, Chence Shi and Zuobai Zhang, Kexin Huang

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