7 research outputs found
Unlocking the Future of Drug Development:Generative AI, Digital Twins, and Beyond
This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery
Linking generative semi-supervised learning and generative open-set recognition
This study investigates the relationship between semi-supervised learning
(SSL) and open-set recognition (OSR) in the context of generative adversarial
networks (GANs). Although no previous study has formally linked SSL and OSR,
their respective methods share striking similarities. Specifically, SSL-GANs
and OSR-GANs require their generators to produce samples in the complementary
space. Subsequently, by regularising networks with generated samples, both SSL
and OSR classifiers generalize the open space. To demonstrate the connection
between SSL and OSR, we theoretically and experimentally compare
state-of-the-art SSL-GAN methods with state-of-the-art OSR-GAN methods. Our
results indicate that the SSL optimised margin-GANs, which have a stronger
foundation in literature, set the new standard for the combined SSL-OSR task
and achieves new state-of-other art results in certain general OSR experiments.
However, the OSR optimised adversarial reciprocal point (ARP)-GANs still
slightly out-performed margin-GANs at other OSR experiments. This result
indicates unique insights for the combined optimisation task of SSL-OSR
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
In an effort to further advance semi-supervised generative and classification
tasks, we propose a simple yet effective training strategy called dual pseudo
training (DPT), built upon strong semi-supervised learners and diffusion
models. DPT operates in three stages: training a classifier on partially
labeled data to predict pseudo-labels; training a conditional generative model
using these pseudo-labels to generate pseudo images; and retraining the
classifier with a mix of real and pseudo images. Empirically, DPT consistently
achieves SOTA performance of semi-supervised generation and classification
across various settings. In particular, with one or two labels per class, DPT
achieves a Fr\'echet Inception Distance (FID) score of 3.08 or 2.52 on ImageNet
256x256. Besides, DPT outperforms competitive semi-supervised baselines
substantially on ImageNet classification tasks, achieving top-1 accuracies of
59.0 (+2.8), 69.5 (+3.0), and 74.4 (+2.0) with one, two, or five labels per
class, respectively. Notably, our results demonstrate that diffusion can
generate realistic images with only a few labels (e.g., <0.1%) and generative
augmentation remains viable for semi-supervised classification. Our code is
available at https://github.com/ML-GSAI/DPT.Comment: Accepted to NeurIPS 202