Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models

Abstract

The development of scanning probe microscopy (SPM) has enabled unprecedented scientific discoveries through high-resolution imaging. Simulations and theoretical analysis of SPM images are equally important as obtaining experimental images since their comparisons provide fruitful understandings of the structures and physical properties of the investigated systems. So far, SPM image simulations are conventionally based on quantum mechanical theories, which can take several days in tasks of large-scale systems. Here, we have developed a scanning tunneling microscopy (STM) molecular image simulation and analysis framework based on a generative adversarial model, CycleGAN. It allows efficient translations between STM data and molecular models. Our CycleGAN-based framework introduces an approach for high-fidelity STM image simulation, outperforming traditional quantum mechanical methods in efficiency and accuracy. We envision that the integration of generative networks and high-resolution molecular imaging opens avenues in materials discovery relying on SPM technologies.

GPT like transformer based conditional molecule generator and a high drug-likeness (QED) dataset generation

“…In the burgeoning field of molecular design, the application of machine learning (ML) techniques has revolutionized the way scientists generate and optimize novel chemical entities. Various ML architectures, including RNNbased [2,3,4] , LSTM-based [5,6,7], Transformer-based [8,9,10], Variational Autoencoders (VAE) [11,12,13,14], and Generative Adversarial Networks (GANs) [15,16,17], along with specialized models like conditional Generative Pre-trained Transformers (GPT), offer unique capabilities and challenges in the generation of molecular structures.…”