Recent advancements in biology and chemistry have leveraged multi-modal learning,
integrating molecules and their natural language descriptions to enhance drug discovery …

We study the problem of training neural stochastic differential equations, or diffusion models,
to sample from a Boltzmann distribution without access to target samples. Existing methods …

High-content phenotypic screening, including high-content imaging (HCI), has gained
popularity in the last few years for its ability to characterize novel therapeutics without prior …

Generative Flow Networks (GFlowNets) are a class of generative models that sample
objects in proportion to a specified reward function through a learned policy. They can be …

Generative models hold great promise for small molecule discovery, significantly increasing
the size of search space compared to traditional in silico screening libraries. However, most …

Without any assumptions about data generation, multiple causal models may explain our
observations equally well. To avoid selecting a single arbitrary model that could result in …

Understanding transition paths between meta-stable states in molecular systems is
fundamental for material design and drug discovery. However, sampling these paths via …

In this thesis, I explored the use of several machine learning techniques, including neural
networks, simulation-based inference, and generative flow networks, on predicting …

Causal reasoning is at the center of the human intellectual abilities that allow us to transfer
our acquired knowledge in situations that are very different from our past experience from …

Generative Flow Networks (GFlowNets) are probabilistic models predicated on Markov
flows, employing specific amortization algorithms to learn stochastic policies that generate …