Portrait de Doina Precup

Doina Precup

Membre académique principal
precupdo@mila.quebec
Chaire en IA Canada-CIFAR
Professeure agrégée, McGill University, École d'informatique
Chef d'équipe de recherche, Google DeepMind
Sujets de recherche
Apprentissage automatique médical
Apprentissage par renforcement
Modèles probabilistes
Modélisation moléculaire
Raisonnement

Biographie

Doina Precup enseigne à l'Université McGill tout en menant des recherches fondamentales sur l'apprentissage par renforcement, notamment les applications de l'IA dans des domaines ayant des répercussions sociales, tels que les soins de santé. Elle s'intéresse à la prise de décision automatique dans des situations d'incertitude élevée.

Elle est membre de l'Institut canadien de recherches avancées (CIFAR) et de l'Association pour l'avancement de l'intelligence artificielle (AAAI), et dirige le bureau montréalais de DeepMind.

Ses spécialités sont les suivantes : intelligence artificielle, apprentissage machine, apprentissage par renforcement, raisonnement et planification sous incertitude, applications.

Étudiants actuels

Safa Alver
Doctorat - McGill
alversaf@mila.quebec
Samin Yeasar Arnob
Doctorat - McGill
Site web
Google Scholar
Sumana Basu
Doctorat - McGill
Co-superviseur⋅e :
Adriana Romero Soriano
Site web
Github
Google Scholar
Veronica Chelu
Doctorat - McGill
Github
Lynn Cherif
Maîtrise recherche - McGill
Co-superviseur⋅e :
Khimya Khetarpal
Site web
Google Scholar
Raymond Chua
Doctorat - McGill
Co-superviseur⋅e :
Blake Richards
Site web
Google Scholar
Wesley Chung
Doctorat - McGill
Superviseur⋅e principal⋅e :
David Meger
Jonathan Colaço Carr
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Prakash Panangaden
Élodie Coté-Gauthier
Stagiaire de recherche - McGill
Derek Kweku DEGBEDZUI Degbedzui
Maîtrise recherche - McGill
Github
Google Scholar
Franco Del Balso
Stagiaire de recherche - UdeM
Github
Manuel Del Verme
Doctorat - McGill
Jesse Farebrother
Doctorat - McGill
Superviseur⋅e principal⋅e :
Marc Gendron-Bellemare
Site web
Github
Google Scholar
Tugce Gurbuz
Doctorat - McGill
Superviseur⋅e principal⋅e :
Eilif Benjamin Muller
Site web
Github
Google Scholar
Howard Huang
Doctorat - McGill
Haque Ishfaq
Doctorat - McGill
Site web
Github
Google Scholar
Mohammad Sami Nur Islam Islam
Maîtrise recherche - McGill
Github
Google Scholar
Arushi Jain
Doctorat - McGill
Site web
Github
Google Scholar
Thomas Jiralerspong
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Yoshua Bengio
Site web
Github
Google Scholar
Martin Klissarov
Doctorat - McGill
Site web
Github
Google Scholar
Flemming Kondrup
Postdoctorat - McGill
Google Scholar
Elaine Lau
Maîtrise recherche - McGill
Jonathan Lebensold
Collaborateur·rice alumni - McGill
Site web
Github
Google Scholar
Ray Luo
Doctorat - McGill
Superviseur⋅e principal⋅e :
Xujie Si
Site web
Github
Google Scholar
G McCracken
Doctorat - McGill
Google Scholar
Nazanin Mohammadi Sepahvand
Doctorat - McGill
Shahrad Mohammadzadeh
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Reihaneh Rabbany
Site web
Github
Google Scholar
Gabriela Moisescu-Pareja
Maîtrise recherche - McGill
Girdhar Neil Girdhar
Collaborateur·rice de recherche - McGill
Padideh Nouri
Doctorat - UdeM
Co-superviseur⋅e :
Yoshua Bengio
Site web
Github
Google Scholar
Charles Onu
Doctorat - McGill
Site web
Github
Google Scholar
Gandharv Patil
Doctorat - McGill
Co-superviseur⋅e :
Pablo Samuel Castro
Site web
Github
Google Scholar
Nate Rahn
Doctorat - McGill
Superviseur⋅e principal⋅e :
Marc Gendron-Bellemare
Sahand Rezaei-Shoshtari
Doctorat - McGill
Co-superviseur⋅e :
David Meger
Site web
Github
Google Scholar
Mandana Samiei
Doctorat - McGill
Co-superviseur⋅e :
Blake Richards
samiemandana@gmail.com
Site web
Github
Google Scholar
Manoosh Samiei
Doctorat - McGill
Co-superviseur⋅e :
Paul Masset
Site web
Github
Google Scholar
Shishir Sharma
Doctorat - McGill
Site web
Github
Google Scholar
Nishanth Anand Vemgal
Doctorat - McGill
Site web
Github
Google Scholar
David Anthony Venuto
Doctorat - McGill
Priyesh Vijayan
Doctorat - McGill
Co-superviseur⋅e :
Samira Ebrahimi Kahou
Site web
Github
Google Scholar
Guangyuan Wang
Stagiaire de recherche - McGill
Steve Wen
Baccalauréat - McGill
Co-superviseur⋅e :
Gregory Dudek
Zijing Wu
Collaborateur·rice de recherche - McGill
Co-superviseur⋅e :
Paul Masset
Site web
Github
Google Scholar
Shuyuan Zhang
Doctorat - McGill
Github
Harry Zhao
Doctorat - McGill
Co-superviseur⋅e :
Yoshua Bengio
Site web
Github
Google Scholar

Publications

Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
Doina Precup
Shuangjia Zheng
The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interact… (voir plus)ion prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme.
2024-11-10
ArXiv (prépublication)
arxiv.org
arxiv.org
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
Doina Precup
Shuangjia Zheng
The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interact… (voir plus)ion prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme.
2024-11-10
ArXiv (prépublication)
doi.org
arxiv.org
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
Doina Precup
Shuangjia Zheng
The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interact… (voir plus)ion prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme.
2024-11-10
ArXiv (prépublication)
arxiv.org
arxiv.org
Reaction-conditioned De Novo Enzyme Design with GENzyme
Chenqing Hua
Jiarui Lu
Yong Liu
Odin Zhang
Jian Tang
Rex Ying
Wengong Jin
Guy Wolf
Doina Precup
Shuangjia Zheng
The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interact… (voir plus)ion prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme.
2024-11-10
ArXiv (prépublication)
doi.org
arxiv.org
Soft Condorcet Optimization for Ranking of General Agents
Marc Lanctot
Kate Larson
Michael Kaisers
Quentin Berthet
Ian Gemp
Manfred Diaz
Roberto-Rafael Maura-Rivero
Yoram Bachrach
Anna Koop
Doina Precup
A common way to drive progress of AI models and agents is to compare their performance on standardized benchmarks. Comparing the performance… (voir plus) of general agents requires aggregating their individual performances across a potentially wide variety of different tasks. In this paper, we describe a novel ranking scheme inspired by social choice frameworks, called Soft Condorcet Optimization (SCO), to compute the optimal ranking of agents: the one that makes the fewest mistakes in predicting the agent comparisons in the evaluation data. This optimal ranking is the maximum likelihood estimate when evaluation data (which we view as votes) are interpreted as noisy samples from a ground truth ranking, a solution to Condorcet's original voting system criteria. SCO ratings are maximal for Condorcet winners when they exist, which we show is not necessarily true for the classical rating system Elo. We propose three optimization algorithms to compute SCO ratings and evaluate their empirical performance. When serving as an approximation to the Kemeny-Young voting method, SCO rankings are on average 0 to 0.043 away from the optimal ranking in normalized Kendall-tau distance across 865 preference profiles from the PrefLib open ranking archive. In a simulated noisy tournament setting, SCO achieves accurate approximations to the ground truth ranking and the best among several baselines when 59\% or more of the preference data is missing. Finally, SCO ranking provides the best approximation to the optimal ranking, measured on held-out test sets, in a problem containing 52,958 human players across 31,049 games of the classic seven-player game of Diplomacy.
2024-11-01
arXiv (publié)
doi.org
arxiv.org
Soft Condorcet Optimization for Ranking of General Agents
Marc Lanctot
Kate Larson
Michael Kaisers
Quentin Berthet
Ian Gemp
Manfred Diaz
Roberto-Rafael Maura-Rivero
Yoram Bachrach
Anna Koop
Doina Precup
A common way to drive progress of AI models and agents is to compare their performance on standardized benchmarks. Comparing the performance… (voir plus) of general agents requires aggregating their individual performances across a potentially wide variety of different tasks. In this paper, we describe a novel ranking scheme inspired by social choice frameworks, called Soft Condorcet Optimization (SCO), to compute the optimal ranking of agents: the one that makes the fewest mistakes in predicting the agent comparisons in the evaluation data. This optimal ranking is the maximum likelihood estimate when evaluation data (which we view as votes) are interpreted as noisy samples from a ground truth ranking, a solution to Condorcet's original voting system criteria. SCO ratings are maximal for Condorcet winners when they exist, which we show is not necessarily true for the classical rating system Elo. We propose three optimization algorithms to compute SCO ratings and evaluate their empirical performance. When serving as an approximation to the Kemeny-Young voting method, SCO rankings are on average 0 to 0.043 away from the optimal ranking in normalized Kendall-tau distance across 865 preference profiles from the PrefLib open ranking archive. In a simulated noisy tournament setting, SCO achieves accurate approximations to the ground truth ranking and the best among several baselines when 59\% or more of the preference data is missing. Finally, SCO ranking provides the best approximation to the optimal ranking, measured on held-out test sets, in a problem containing 52,958 human players across 31,049 games of the classic seven-player game of Diplomacy.
2024-10-31
ArXiv (prépublication)
doi.org
arxiv.org
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor W. Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-10-13
NeurIPS.cc/2024/Workshop/AIDrugX (poster)
doi.org
openreview.net
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
Chenqing Hua
Yong Liu
Dinghuai Zhang
Odin Zhang
Sitao Luan
Kevin K Yang
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-10-13
NeurIPS.cc/2024/Workshop/AIDrugX (poster)
doi.org
openreview.net
Identifying and Addressing Delusions for Target-Directed Decision-Making
Harry Zhao
Mingde Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
We are interested in target-directed agents, which produce targets during decision-time planning, to guide their behaviors and achieve bette… (voir plus)r generalization during evaluation. Improper training of these agents can result in delusions: the agent may come to hold false beliefs about the targets, which cannot be properly rejected, leading to unwanted behaviors and damaging out-of-distribution generalization. We identify different types of delusions by using intuitive examples in carefully controlled environments, and investigate their causes. We demonstrate how delusions can be addressed for agents trained by hindsight relabeling, a mainstream approach in for training target-directed RL agents. We validate empirically the effectiveness of the proposed solutions in correcting delusional behaviors and improving out-of-distribution generalization.
2024-10-12
NeurIPS.cc/2024/Workshop/SafeGenAi (poster)
doi.org
openreview.net
Mitigating Downstream Model Risks via Model Provenance
Keyu Wang
Abdullah Norozi Iranzad
Scott Schaffter
Doina Precup
Jonathan Lebensold
Meg Risdal
Research and industry are rapidly advancing the innovation and adoption of foundation model-based systems, yet the tools for managing these … (voir plus)models have not kept pace. Understanding the provenance and lineage of models is critical for researchers, industry, regulators, and public trust. While model cards and system cards were designed to provide transparency, they fall short in key areas: tracing model genealogy, enabling machine readability, offering reliable centralized management systems, and fostering consistent creation incentives. This challenge mirrors issues in software supply chain security, but AI/ML remains at an earlier stage of maturity. Addressing these gaps requires industry-standard tooling that can be adopted by foundation model publishers, open-source model innovators, and major distribution platforms. We propose a machine-readable model specification format to simplify the creation of model records, thereby reducing error-prone human effort, notably when a new model inherits most of its design from a foundation model. Our solution explicitly traces relationships between upstream and downstream models, enhancing transparency and traceability across the model lifecycle. To facilitate the adoption, we introduce the unified model record (UMR) repository , a semantically versioned system that automates the publication of model records to multiple formats (PDF, HTML, LaTeX) and provides a hosted web interface (https://modelrecord.com/). This proof of concept aims to set a new standard for managing foundation models, bridging the gap between innovation and responsible model management.
2024-10-09
NeurIPS.cc/2024/Workshop/SoLaR (poster)
doi.org
openreview.net
Rejecting Hallucinated State Targets during Planning
Mingde Zhao
Tristan Sylvain
Romain Laroche
Doina Precup
Yoshua Bengio
2024-10-09
ArXiv (prépublication)
arxiv.org
arxiv.org
Reactzyme: A Benchmark for Enzyme-Reaction Prediction
Chenqing Hua
Bozitao Zhong
Sitao Luan
Liang Hong
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)
doi.org
openreview.net