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Multi-Scale Representation Learning for Protein Fitness Prediction
NeurIPS, 2024. A multimodal framework that integrates protein sequence, structure, and surface topology features to achieve state-of-the-art fitness prediction.
ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design
NeurIPS, 2023. Large-scale benchmarks to assess models for protein fitness prediction and design.
ProteinNPT: Improving Protein Property Prediction and Design with Non-Parametric Transformers
NeurIPS, 2023. A conditional semi-supervised pseudo-generative model for fitness prediction and design.
DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design
ICML, 2023. A sample-efficient method for discovering optimal sets that are both diverse and optimize the function of interest.
Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval
ICML, 2022. A suite of autoregressive transformers with biological priors, augmented with inference-time retrieval, to achieve SOTA performance on protein fitness prediction.
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
ICLR, 2022. A benchmark suite for evaluating active learning algorithms for experimental design in drug discovery.
Improving Black-box Optimization in VAE Latent Space Using Decoder Uncertainty
NeurIPS, 2021. A framework that uses the epistemic uncertainty of the decoder of a VAE to guide the optimization of properties of high-dimensional structured objects (e.g., molecules) in latent space.