The paper introduces UAE-3D, a multi-modal VAE for 3D molecule generation that compresses molecules into a unified latent space, maintaining near-zero reconstruction error. This approach simplifies latent diffusion modeling by eliminating the need to handle multi-modality and equivariance separately. Experiments on GEOM-Drugs and QM9 datasets show UAE-3D establishes new benchmarks in de novo and conditional 3D molecule generation, with significant improvements in efficiency and quality.
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.
A DeepMind researcher presented work on incorporating symmetries into machine learning models, with applications to lattice-QCD and molecular dynamics. The work includes permutation and translation-invariant normalizing flows for free-energy estimation in molecular dynamics. They also presented U(N) and SU(N) Gauge-equivariant normalizing flows for pure Gauge simulations and its extensions to incorporate fermions in lattice-QCD. Why it matters: Applying symmetry principles to generative models could improve AI's ability to model complex physical systems relevant to materials science and other fields in the region.
Daisuke Kihara from Purdue University presented a seminar at MBZUAI on using deep learning for biomolecular structure modeling. His lab is developing 3D structure modeling methods, especially for cryo-electron microscopy (cryo-EM) data. They are also working on RNA structure prediction and peptide docking using deep neural networks inspired by AlphaFold2. Why it matters: Applying advanced deep learning techniques to biomolecular structure prediction can accelerate drug discovery and our understanding of molecular functions.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.
MBZUAI's Metaverse Center is developing technologies for realistic avatar generation. Hao Li and colleagues presented a novel approach at CVPR 2024, collaborating with ETH Zurich, VinAI Research, and Pinscreen. The technology addresses the challenge of mapping 2D images to 3D avatars, accounting for poses, expressions, and views. Why it matters: Creating realistic and efficient avatar generation could improve user experience and accessibility in virtual environments across the Middle East.
MBZUAI researchers have developed MorphDiff, a diffusion model that predicts cell morphology from gene expression data. MorphDiff uses the transcriptome to generate realistic post-perturbation images, either from scratch or by transforming a control image. The model combines a Morphology Variational Autoencoder (MVAE) with a Latent Diffusion Model, enabling both gene-to-image generation and image-to-image transformation. Why it matters: This could significantly accelerate drug discovery and biological research by allowing scientists to preview cellular changes before conducting experiments.
KAUST researchers are exploring novel chemical reactors and separation processes using mathematical design, with a focus on time and shape variables to enhance transport, heat transfer, and mass transfer. By aligning design, modeling, and 3D printing, they create customized shapes with great complexity and less material. This approach allows for the creation of bespoke reactors and separation processes tailored to specific applications, improving efficiency and reducing energy consumption. Why it matters: This research demonstrates the potential of advanced manufacturing techniques to revolutionize industrial design in the Middle East's chemical and pharmaceutical sectors.