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Machine Learning - Research Internship

Résumé du poste
Stage
Paris
Salaire : Non spécifié
Télétravail fréquent
Compétences & expertises
Contenu généré
Tensorflow
Pytorch
Python

AQEMIA
AQEMIA

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Le poste

Descriptif du poste

About Aqemia

AQEMIA is a next-gen techbio generating one of the world's fastest-growing drug discovery pipelines. Our mission is to design fast many innovative drug candidates for critical diseases. Our differentiation lies in our unique quantum and statistical mechanics algorithms to power a generative artificial intelligence designing novel drug candidates, without the need to train on experimental data. We already delivered several drug discovery successes in the internal pipeline as well as in collaboration with Pharma companies - most advanced programs being currently in vivo optimization.

About the team you will join

As a Machine Learning Research Intern, you’ll join a team of engineers and researchers building algorithms to improve and accelerate our internal drug discovery pipeline. You will be working in the series-expansion team, composed of 3 ML engineers. On a day-to-day basis, you will interact with Victor Saillant.

Your role

You will explore the topic of molecular generation in depth and be responsible for literature review, implementation and training/evaluation of models on public and proprietary data. 

Your internship should last between 4 and 6 months, and can start as early as possible in 2024.

Subject of the internship

The objective of the internship is to address the problem of molecule generation conditioned on a protein, and possibly in a constrained chemical space and additional physico-chemical properties. The proposed method involves the use of diffusion models on graphs to address this issue (see references [1][2]). Additionally, alternative approaches, like auto-regressive models, may be explored in a subsequent phase (see references [3][4]).

Skills

  • You are a Masters student or a PhD student in Computer Science, Applied Mathematics, Bioinformatics, or a related field.
  • You are actively interested in the field of machine learning, and enjoy keeping up to date with current developments.
  • Your knowledge of mathematics and statistics allows you to understand and critically evaluate research papers from the field.
  • You are comfortable with Python as a programming language, and ideally have hands-on experience with the implementation (using PyTorch/Jax/Tensorflow), training, and evaluation of deep learning systems.
  • You are curious and eager to spend time learning new topics from people with diverse backgrounds, and believe that machine learning can play a pivotal role in biology and chemistry for drug discovery.
  • Nice to have

  • Experience in representation learning, generative modeling.
  • You’re interested in complex structured data such as graphs, point clouds, and text.
  • Knowledge in biology and/or chemistry/chemoinformatics is a strong plus.
  • References

  • [1] Huang, L., Xu, T., Yu, Y. et al. “A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets”. Nat Commun 15, 2657 (2024). https://doi.org/10.1038/s41467-024-46569-1
  • [2] Schneuing, Arne, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell et al. "Structure-based drug design with equivariant diffusion models." arXiv preprint arXiv:2210.13695 (2022).
  • [3] Zhung, W., Kim, H. & Kim, W.Y. 3D molecular generative framework for interaction-guided drug design. Nat Commun 15, 2688 (2024). https://doi.org/10.1038/s41467-024-47011-2
  • [4] Alexander S. Powers, Helen H. Yu, Patricia Suriana, Rohan V. Koodli, Tianyu Lu, Joseph M. Paggi, and Ron O. Dror. Geometric Deep Learning for Structure-Based Ligand Design ACS Central Science 2023 9 (12), 2257-2267 DOI: 10.1021/acscentsci.3c00572 https://pubs.acs.org/doi/full/10.1021/acscentsci.3c00572
  • About us

    We work for a mission: joining us means having your own impact on changing the way drugs are discovered, and helping to shape the direction of our fast-growing company and team.

    🚀 Mission-Driven: We want to find many drugs and change drug discovery paradigm

    💊 Fast-growing drug discovery pipe and proofs: Internal pipeline with multiple programs against cancer for now, most advanced ones currently in vivo proof of concept/ patent stage + Collaborations with several top Pharma companies such as Sanofi, J&J (...) incl. 140M$ deal with Sanofi

    👥 Interdisciplinary world-Class team: built a team of 60 top tier people at the crossroads of tech and life sciences

    👤 Experienced Founders: 15+ years experience including resp. leading research at ENS, Oxford, Cambridge and career in strategy con>sulting at BCG.

    💫 DeepTech Innovators: Part of the French Tech 120 and France 2030, spinoff of Ecole Normale Superieure and CNRS

    🏢 Prime Location: Paris (1 Bd Pasteur) with 2 days of remote per week.

    🌎 International environment: Our working language is English. We offer a relocation package for those who wish to join us in France, which includes assistance in finding an apartment, help with all administrative tasks, and optional French lessons.

    💸 Fundraising: $60M with European tech/deeptech funds - Elaia Partners (French leader deeptech VC), Eurazeo (largest European investment fund), Bpifrance Large Venture (largest French fund and fund of funds) and Wendel (PE tech fund).

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