Recent research in Generative AI is rapidly transforming the frontier of research in computational structural biology. The successes have substantially advanced protein design and drug discovery. One of the key methodologies underlying these advances is diffusion models (DM). DMs are well-equipped to model high dimensional, geometric data while exploiting key strengths of deep learning.
Diffusion models (DM) are a class of probabilistic generative models where the generation process consists of progressively modifying noisy samples toward clean examples. The model learns to interpolate between two distributions, a simple noise distribution that is easy to sample from (typically a Gaussian), and the desired data distribution. The neural network then has the ability to progressively transform noise samples toward data.
Currently, there’s a notable interest in employing sophisticated computer models, known as generative models, to craft fresh designs for protein structures. These models also have the capability to generate specific protein structures tailored for various applications.
Recent research focuses on the generative applications of protein structures and docking, which includes
*Choices around various representations for 3D molecular data
*Modeling tasks
*Heuristic assumptions in each approach
*Best practices for evaluation.
With improved neural network architectures on all-atom systems and improved utilization of all-atom data. Better protein interaction predictions will lead to compelling in silico evaluations of generative capabilities, further enabling computational researchers to rigorously assess new methods.
DMs are also transforming well-established tasks such as molecular docking, opening ways to address previously largely intractable problems like blind or flexible docking. However, there are still some important limitations. These include the need to enhance the accuracy of detailed binding positions, especially when dealing with new receptors and compounds that the model hasn’t encountered before.
Machine learning researchers are actively investigating extensions involving flow matching, Schrödinger bridges, and stochastic interpolants. We anticipate that the use of generative approaches will continue to be a fundamental modeling principle.
To know more about the applications of DM approaches that have
achieved state-of-the-art performance, https://lnkd.in/gRJDqQ_M
