AntiBARTy Diffusion for Property Guided Antibody Design
- URL: http://arxiv.org/abs/2309.13129v1
- Date: Fri, 22 Sep 2023 18:30:50 GMT
- Title: AntiBARTy Diffusion for Property Guided Antibody Design
- Authors: Jordan Venderley
- Abstract summary: We train an antibody-specific language model, AntiBARTy, based on BART (Bidirectional and Auto-Regressive Transformer) and use its latent space to train a property-conditional diffusion model for guided IgG de novo design.
As a test case, we show that we can effectively generate novel antibodies with improved in-silico solubility while maintaining antibody validity and controlling sequence diversity.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Over the past decade, antibodies have steadily grown in therapeutic
importance thanks to their high specificity and low risk of adverse effects
compared to other drug modalities. While traditional antibody discovery is
primarily wet lab driven, the rapid improvement of ML-based generative modeling
has made in-silico approaches an increasingly viable route for discovery and
engineering. To this end, we train an antibody-specific language model,
AntiBARTy, based on BART (Bidirectional and Auto-Regressive Transformer) and
use its latent space to train a property-conditional diffusion model for guided
IgG de novo design. As a test case, we show that we can effectively generate
novel antibodies with improved in-silico solubility while maintaining antibody
validity and controlling sequence diversity.
Related papers
- Large scale paired antibody language models [40.401345152825314]
We present IgBert and IgT5, the best performing antibody-specific language models developed to date.
These models are trained comprehensively using the more than two billion Observed Space dataset.
This advancement marks a significant leap forward in leveraging machine learning, large data sets and high-performance computing for enhancing antibody design for therapeutic development.
arXiv Detail & Related papers (2024-03-26T17:21:54Z) - Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization [51.28231365213679]
We tackle antigen-specific antibody sequence-structure co-design as an optimization problem towards specific preferences.
We propose direct energy-based preference optimization to guide the generation of antibodies with both rational structures and considerable binding affinities to given antigens.
arXiv Detail & Related papers (2024-03-25T09:41:49Z) - xTrimoABFold: De novo Antibody Structure Prediction without MSA [77.47606749555686]
We develop a novel model named xTrimoABFold to predict antibody structure from antibody sequence.
The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame-aligned point loss.
arXiv Detail & Related papers (2022-11-30T09:26:08Z) - Incorporating Pre-training Paradigm for Antibody Sequence-Structure
Co-design [134.65287929316673]
Deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences.
The computational methods heavily rely on high-quality antibody structure data, which is quite limited.
Fortunately, there exists a large amount of sequence data of antibodies that can help model the CDR and alleviate the reliance on structure data.
arXiv Detail & Related papers (2022-10-26T15:31:36Z) - Reprogramming Pretrained Language Models for Antibody Sequence Infilling [72.13295049594585]
Computational design of antibodies involves generating novel and diverse sequences, while maintaining structural consistency.
Recent deep learning models have shown impressive results, however the limited number of known antibody sequence/structure pairs frequently leads to degraded performance.
In our work we address this challenge by leveraging Model Reprogramming (MR), which repurposes pretrained models on a source language to adapt to the tasks that are in a different language and have scarce data.
arXiv Detail & Related papers (2022-10-05T20:44:55Z) - AntBO: Towards Real-World Automated Antibody Design with Combinatorial
Bayesian Optimisation [53.43922443725598]
We present AntBO: a Combinatorial optimisation algorithm enabling efficient in silico design of the CDRH3 region.
To benchmark AntBO, we use the Absolut! software suite as a black-box oracle because it can score the target specificity and affinity of designed antibodies in silico.
In under 200 protein designs, AntBO can suggest antibody sequences that outperform the best binding sequence drawn from 6.9 million experimentally obtained CDRH3s.
arXiv Detail & Related papers (2022-01-29T12:03:04Z) - Accelerating Antimicrobial Discovery with Controllable Deep Generative
Models and Molecular Dynamics [109.70543391923344]
CLaSS (Controlled Latent attribute Space Sampling) is an efficient computational method for attribute-controlled generation of molecules.
We screen the generated molecules for additional key attributes by using deep learning classifiers in conjunction with novel features derived from atomistic simulations.
The proposed approach is demonstrated for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency.
arXiv Detail & Related papers (2020-05-22T15:57:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.