Multi-domain Distribution Learning for De Novo Drug Design

To further enhance the sampling process towards distribution regions with desirable metric values, we propose a joint preference alignment scheme applicable to both flow matching and Markov bridge frameworks. Furthermore, we extend our model to also explore the conformational landscape of the protein by jointly sampling side chain angles and molecules. Small molecules are the predominant class of FDA-approved drugs with a share of 85%, and more than 95% of known drugs target human or pathogen proteins (Santos et al., 2017). At the same time, the cost and duration of the development of new drugs are skyrocketing (Simoens & Huys, 2021). This sparks increasing interest in the computational design of small molecular compounds that bind specifically to disease-associated proteins and thus reduce the amount of costly experimental testing. In recent years, the machine learning community has contributed a plethora of generative tools addressing drug design from various angles (Du et al., 2024). However, these methods typically require careful tuning of the objective function to avoid exploiting imperfect computational oracles and overly maximizing one desired property (e.g. Additionally, one often aims to design a suitable 3D binding pose along with the chemical structure of the molecule, which substantially increases the degrees of freedom. Many optimization algorithms struggle to efficiently navigate such vast design spaces. Following a different approach, probabilistic generative models learn to generate drug-like molecules directly from data (Hoogeboom et al., 2022; Vignac et al., 2022). Here, the design objectives are implicitly encoded in the training data set. While these methods may not outperform direct optimization on isolated metrics, they are well suited for the multifaceted nature of drug design as they learn "what a drug looks like" in a more general way. Once trained on sufficient high-quality data, these models can capture a more holistic picture of the molecular space compared to models optimized for a limited set of target metrics. The strength of generative modeling lies in its ability to reproduce patterns seen in the training data.