The paper introduces Competitive Dimerization Networks (CDNs) as a chemical learning framework where molecular species bind reversibly to form dimers, with binding affinities acting as tunable synaptic weights. Through a directed evolution protocol involving mutation, selection, and amplification of DNA-based components, CDNs can be trained in vitro to perform complex tasks such as multiclass classification, achieving strong output contrast and high mutual information. Comparative studies with in silico gradient descent show closely correlated performance, positioning CDNs as a promising platform for analog physical computation that bridges synthetic biology and machine learning.
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
arXiv:2606. 05198v1 Announce Type: cross Abstract: Nucleic acids are increasingly recognized as therapeutic targets beyond conventional protein-centered drug discovery, yet accurate and efficient docking of small molecules to nucleic acid structures remains challenging.
The paper presents a closed‑loop molecule generation pipeline that iteratively retrains on new quantum‑chemical simulation data, overcoming limitations of static generative models. This approach produces molecules whose properties extend up to 0.44 standard deviations beyond the training set and improves out‑of‑distribution classification accuracy by 79%. By conditioning on thermodynamic stability during the loop, the method yields a 3.5‑fold increase in the proportion of stable, potentially synthesizable molecules.
PGFS++ is a synthesis‑aware reinforcement learning framework that improves molecular properties while ensuring the resulting molecules can be synthesized and remain structurally similar to the input. It builds on PGFS+ by using trainable embedding lookup tables for reaction templates and second reactants, a more effective scoring function, and a refined RL algorithm. Experiments demonstrate that PGFS++ enhances target properties and preserves high output diversity, overcoming the reward‑hacking failure mode seen in earlier versions.
arXiv:2608. 05336v1 Announce Type: cross Abstract: Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships.
arXiv:2607. 20551v1 Announce Type: cross Abstract: Effective molecular representation learning is crucial for accurate molecular property prediction.
arXiv:2608. 02688v1 Announce Type: cross Abstract: Phenotypic drug discovery enables the discovery of functional relationships between molecular structures and cellular responses.
PGFS++ is a synthesis‑aware reinforcement learning framework that improves molecular properties such as drug‑likeness or binding affinity while ensuring the resulting molecules can be synthesized and remain structurally similar to the input. It builds on PGFS+ by using trainable embedding lookup tables for reaction templates and second reactants, a more effective scoring function, and a refined RL algorithm. The method addresses a reward‑hacking failure mode by treating each input molecule as the start of a forward‑synthesis trajectory, applying learned reaction templates with in‑stock building blocks, and producing diverse, high‑quality outputs with explicit synthesis routes.
arXiv:2606. 30170v1 Announce Type: cross Abstract: Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets.
The paper presents a deep learning approach that converts nanopore resistive pulse signals into scaleograms using continuous wavelet transforms, enabling the classification of peptides as an image‑classification problem. On a dataset of 42 peptides, the method achieves an 82% macro‑averaged accuracy, outperforming previous descriptor‑based techniques by 8.6 percentage points. The models also remain accurate after significant weight pruning and 8‑bit quantization, making them suitable for deployment on embedded sensing hardware.
arXiv:2604. 24474v2 Announce Type: replace Abstract: Molecular similarity plays a central role in ligand-based drug discovery, such as virtual screening, analog searching, and goal-directed molecular generation.
arXiv:2510.07289v2 Announce Type: replace Abstract: Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated s...