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Chenghao Liu

Collaborateur·rice alumni
Superviseur⋅e principal⋅e
Yoshua Bengio
Sujets de recherche
Modèles génératifs
Modélisation moléculaire
Site web
Google Scholar
GitHub

Publications

Multi-Fidelity Active Learning with GFlowNets
Alex Hernandez-Garcia
Nikita Saxena
Moksh J. Jain
Cheng-Hao Liu
Yoshua Bengio
In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanw… (voir plus)hile, machine learning has progressed to become a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, structured and high-dimensional spaces. Moreover, the high fidelity, black-box objective function is often very expensive to evaluate. Progress in machine learning methods that can efficiently tackle such challenges would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose a multi-fidelity active learning algorithm with GFlowNets as a sampler, to efficiently discover diverse, high-scoring candidates where multiple approximations of the black-box function are available at lower fidelity and cost. Our evaluation on molecular discovery tasks shows that multi-fidelity active learning with GFlowNets can discover high-scoring candidates at a fraction of the budget of its single-fidelity counterpart while maintaining diversity, unlike RL-based alternatives. These results open new avenues for multi-fidelity active learning to accelerate scientific discovery and engineering design.
2023-06-20
ArXiv (prépublication)
doi.org
arxiv.org
Thompson Sampling for Improved Exploration in GFlowNets
Jarrid Rector-Brooks
Kanika Madan
Moksh J. Jain
Maksym Korablyov
Cheng-Hao Liu
Sarath Chandar
Nikolay Malkin
Yoshua Bengio
Generative flow networks (GFlowNets) are amortized variational inference algorithms that treat sampling from a distribution over composition… (voir plus)al objects as a sequential decision-making problem with a learnable action policy. Unlike other algorithms for hierarchical sampling that optimize a variational bound, GFlowNet algorithms can stably run off-policy, which can be advantageous for discovering modes of the target distribution. Despite this flexibility in the choice of behaviour policy, the optimal way of efficiently selecting trajectories for training has not yet been systematically explored. In this paper, we view the choice of trajectories for training as an active learning problem and approach it using Bayesian techniques inspired by methods for multi-armed bandits. The proposed algorithm, Thompson sampling GFlowNets (TS-GFN), maintains an approximate posterior distribution over policies and samples trajectories from this posterior for training. We show in two domains that TS-GFN yields improved exploration and thus faster convergence to the target distribution than the off-policy exploration strategies used in past work.
2023-06-19
ICML.cc/2023/Workshop/SPIGM (poster)
doi.org
openreview.net
GFlowNets for AI-Driven Scientific Discovery
Moksh J. Jain
Tristan Deleu
Jason Hartford
Cheng-Hao Liu
Alex Hernandez-Garcia
Yoshua Bengio
Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the p… (voir plus)ace of scientific discovery. While science has traditionally relied...
2023-02-01
ArXiv (prépublication)
doi.org
arxiv.org
GFlowNets for AI-Driven Scientific Discovery
Moksh J. Jain
Tristan Deleu
Jason Hartford
Cheng-Hao Liu
Alex Hernandez-Garcia
Yoshua Bengio
Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the p… (voir plus)ace of scientific discovery. While science has traditionally relied...
2023-01-01
Digital Discovery (publié)
doi.org
arxiv.org
RetroGNN: Fast Estimation of Synthesizability for Virtual Screening and De Novo Design by Learning from Slow Retrosynthesis Software
Cheng-Hao Liu
Maksym Korablyov
Stanisław Jastrzębski
Paweł Włodarczyk-Pruszyński
Yoshua Bengio
Marwin Segler
2022-04-22
Journal of Chemical Information and Modeling (publié)
doi.org
E VALUATING G ENERALIZATION IN GF LOW N ETS FOR M OLECULE D ESIGN
Andrei Cristian Nica
Moksh J. Jain
Emmanuel Bengio
Cheng-Hao Liu
Maksym Korablyov
Michael M. Bronstein
Yoshua Bengio
Deep learning bears promise for drug discovery problems such as de novo molecular design. Generating data to train such models is a costly a… (voir plus)nd time-consuming process, given the need for wet-lab experiments or expensive simulations. This problem is compounded by the notorious data-hungriness of machine learning algorithms. In small molecule generation the recently proposed GFlowNet method has shown good performance in generating diverse high-scoring candidates, and has the interesting advantage of being an off-policy offline method. Finding an appropriate generalization evaluation metric for such models, one predictive of the desired search performance (i.e. finding high-scoring diverse candidates), will help guide online data collection for such an algorithm. In this work, we develop techniques for evaluating GFlowNet performance on a test set, and identify the most promising metric for predicting generalization. We present empirical results on several small-molecule design tasks in drug discovery, for several GFlowNet training setups, and we find a metric strongly correlated with diverse high-scoring batch generation. This metric should be used to identify the best generative model from which to sample batches of molecules to be evaluated.
2022-04-05
ICLR.cc/2022/Workshop/MLDD (poster)
openreview.net
openreview.net
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
Cheng-Hao Liu
Maksym Korablyov
Stanisław Jastrzębski
Paweł Włodarczyk-Pruszyński
Yoshua Bengio
Marwin Segler
De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search … (voir plus)process towards more easily synthesizable molecules using a proxy for synthetic accessibility. However, using currently available proxies still results in highly unrealistic compounds. We investigate the feasibility of training deep graph neural networks to approximate the outputs of a retrosynthesis planning software, and their use to bias the search process. We evaluate our method on a benchmark involving searching for drug-like molecules with antibiotic properties. Compared to enumerating over five million existing molecules from the ZINC database, our approach finds molecules predicted to be more likely to be antibiotics while maintaining good drug-like properties and being easily synthesizable. Importantly, our deep neural network can successfully filter out hard to synthesize molecules while achieving a
2020-11-25
ArXiv (prépublication)
arxiv.org
arxiv.org