arXiv Machine Learning
Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation
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Navigating Route Latent Space for Synthesizable Molecular Design
arXiv:2610.07560v1 Announce Type: new Abstract: Goal-directed molecular design has advanced rapidly, yet a substantial proportion of designed molecules remain difficult to synthesize in practice, lim...
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A Large Scale Investigation of Scaling Limits in Chemical Language Models
The paper reports a large-scale, compute-controlled study of Chemical Language Models (CLMs) involving over 30,000 experiments across different molecular representations, tokenizations, model sizes, datasets, and architectures. It finds clear scaling trends in pretraining loss but shows that these improvements do not translate into proportional gains in goal-directed molecular design, with chemical syntax saturating early while semantic properties develop more slowly. The authors release a new suite of models, NovoMolGen, that achieves state-of-the-art results in drug discovery tasks, highlighting a disconnect between representation learning and downstream design and calling for new pretraining paradigms that target chemical semantics.
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NEAT-POCKET: Pocket-Conditioned Autoregressive 3D Molecular Generation with a Neighborhood-Guided Set Transformer
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arXiv Machine Learning
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ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation
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Do Language Models Dream of Binding Molecules? Benchmarking LLMs under Spatial Constraints
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arXiv Machine Learning
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Molecular representation shapes the balance between target fidelity and exploration in flow based polymer generation
The paper introduces PolyLatentFlow, a continuous‑time flow‑matching framework for polymer generation, and LlamaUni, a multimodal representation that fuses polymer sequences with 3D structural data. In unconditional generation, the combination yields the highest number of valid, novel candidates while preserving diversity, and in conditional settings it systematically shifts property distributions across a 200 °C target range. Across multi‑property tasks, the representation choice affects validity, training‑set replay, and structural proximity, with PolyLatentFlow + LlamaUni achieving the best balance of high validity, low replay, and high target hit yield.