A hierarchical Bayesian multitask learning model that is applicable to the general multi-task binary classification learning problem where the model assumes a shared sparsity structure across different tasks is proposed and derived based on variational inference to approximate the posterior distribution.
Hao-Nan Zhu, Andre R. Goncalves, Car Reen Kok et al.· BioData Mining· 1 citation
Many molecular Transformers lack probabilistic latent variables for posterior inference and latent interpolation. We introduce STAR-VAE, a SELFIES-encoded, Transformer-based, AutoRegressive Variational AutoEncoder combining a bidirectional encoder with an autoregressive decoder pretrained on 79 million PubChem molecule...
Bum Chul Kwon, Ben Shapira, Moshiko Raboh et al.· 0 citations
Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to virtual screening, a key challenge is how to use limited experimental data from the target assay to improve the prioritization of active compounds. We investigated whether f...
This work develops a systematic understanding of how physical patterns can alternatively be discovered directly from data by training a model without domain-specific priors, including any manually defined atomistic pairwise interactions, and finds that the model autonomously recovers key physical structure.
T. Kreiman, Yu-Tong Bai, Fadi Atieh et al.· 10 citations
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STOMP is a powerful, robust multi-objective alignment algorithm that can meaningfully improve post-training in multiple domains and achieves or ties for the highest hypervolumes on 16/18 protein tasks and 5/6 natural language tasks.
Aadyot Bhatnagar, Peter Mørch Groth, Sebastian Ibarraran et al.· arXiv.org· 0 citations
ReGeoDTA is developed, a representation-preserving framework that maintains affinity-relevant chemical heterogeneity in molecular representations and continuous geometric relationships in protein structures that identifies representation fidelity as an upstream design principle for accurate and generalizable drug--targ...
Yi-Xiao Li, Yi-Ning Qian, Ye-Fan Chen et al.· 0 citations
This work proposes LabAgent, a reproduce and discovery harness tailored for a lab's continuous work that ranks first over commercial generalist agents in every domain, and demonstrates accurate reproduction of a published figure.
Lei Liu, Yi-Kun Zhang, Jia-Lin Chen et al.· 0 citations
GRACE (Geometric Residual Adduct Conditioning via Early-fusion), a 3D CCS predictor that adapts a pretrained molecular geometry encoder using geometric residual adduct conditioning via early fusion, is presented.
Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi et al.· 0 citations
Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator t...
Andrea Zerio, Yi-Song Yao, Alessandro Micheli et al.· 0 citations
The results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives, whereas stronger guidance can degrade geometry and distributional fidelity.
This work provides a scalable framework for predicting oligonucleotide melting behavior across diverse chemistries and assay conditions, supporting more reliable molecular assay design and demonstrating that learned sequence representations can complement classical thermodynamic models by capturing context-dependent ef...
Danielle L. Ferreira, Li-Feng Lin, Adam Aslam et al.· 0 citations
Net charge is a developability-relevant property of therapeutic binders, linked to viscosity, clearance, nonspecific interaction and aggregation, and antibody screens already use charge-related criteria, but inverse-folding pipelines expose no way to set it to a target value.
Mohammed Sameer Syed, Tamara Dinneen· 0 citations
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