AINeutralarXiv – CS AI · Jun 55/10
🧠Researchers propose AMREC, a new agentic framework that improves text-guided molecular generation by shifting focus from merely fixing invalid chemical structures to preserving target-relevant molecular identity. The approach outperforms existing correction strategies by combining molecule-aware tracking with expanded candidate exploration, achieving superior recovery across multiple evaluation metrics on invalid molecular drafts.
AINeutralarXiv – CS AI · Jun 46/10
🧠Researchers introduce MuCO, a generative AI method for modeling cyclic peptide structures through multi-stage conformation optimization. The approach outperforms existing methods in stability, diversity, and efficiency, offering significant implications for computational drug discovery and peptide-based therapeutic development.
AINeutralarXiv – CS AI · Jun 25/10
🧠Researchers introduce ATOM, a multi-agent framework that treats molecular optimization as tree-structured search where specialized agents coordinate across different pathways rather than enforcing consensus. The method demonstrates improved performance on multi-objective molecular design benchmarks by maintaining diverse trade-offs and exploring multiple promising trajectories simultaneously.
$ATOM
AIBullisharXiv – CS AI · Jun 26/10
🧠Researchers introduce PROBE, a novel optimization framework that enables LLM agents to design drugs more effectively by probing molecular structures before making edits. The method addresses a critical failure in current drug-design pipelines: agents often sacrifice druggability when optimizing for binding affinity. PROBE achieves state-of-the-art results on standard benchmarks by mimicking how medicinal chemists strategically explore chemical modifications.
AIBullisharXiv – CS AI · Jun 26/10
🧠Researchers introduce the Bond Smoothness Characterization Test (BSCT), a new evaluation metric for Machine Learning Interatomic Potentials that efficiently detects physical inaccuracies in quantum potential energy surfaces. By combining BSCT with architectural refinements like differentiable k-nearest neighbors and temperature-controlled attention, the team demonstrates how systematic model design can achieve both low regression errors and stable molecular dynamics simulations.
AINeutralarXiv – CS AI · May 296/10
🧠Researchers present a factorial benchmark decomposing 2D molecular message-passing neural networks into 84 distinct configurations to identify which operator components drive molecular property prediction performance. The study finds that message construction methods significantly outweigh update complexity in determining model effectiveness, with concatenation-based mixing showing superior performance in differentiating molecular structures.
AINeutralarXiv – CS AI · May 286/10
🧠MACReD, a multi-agent AI framework, advances chemical reaction diagram parsing from scientific literature by achieving 75.2% F1 score on the RxnScribe benchmark—a 6.1 percentage point improvement over existing baselines. The system combines specialized agents for molecular recognition, arrow detection, and text extraction within a unified vision-language model architecture to handle complex spatial layouts in chemistry research documents.
AINeutralarXiv – CS AI · May 285/10
🧠Researchers present an improved PULSE method for efficiently estimating thermodynamic properties of chemically disordered compounds using AI-driven partition function sampling. The approach significantly reduces computational costs compared to traditional Monte Carlo methods while maintaining high accuracy, as demonstrated through 2D Ising model validation.
AIBullisharXiv – CS AI · May 276/10
🧠Researchers introduce Periodic-TDL, a deep learning framework using topological data analysis to predict polymer properties more accurately than existing models. The approach captures many-body interactions across polymer chains and has been validated against experimental data from newly synthesized polymers, demonstrating practical utility in accelerating polymer discovery.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce EDMolGPT, a generative AI model that uses electron density data from protein binding pockets to design novel drug molecules. The approach improves upon existing methods by incorporating physically grounded density information rather than empty pocket structures, enabling more accurate molecular generation with realistic 3D conformations.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers propose SMER-Opt, a novel approach to molecular optimization that combines a single-step edit response predictor with multi-step planning via tree search. The method addresses the challenge of editing molecules for desired properties by treating molecular edits as discrete actions guided by chemical feasibility rules, reducing dependence on external oracles and improving data efficiency.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers have developed a crystal fractional graph neural network that combines graph neural networks with compositional embeddings to predict the energy of high-entropy alloys, achieving accuracy comparable to first-principles calculations on a dataset of over 1,000 crystal structures. The hybrid architecture addresses a key challenge in materials science by integrating local atomic interactions and global elemental composition, though scalability limitations for larger crystal systems remain.
AINeutralarXiv – CS AI · May 126/10
🧠SLayerGen introduces a generative AI model capable of creating crystal structures constrained to space and layer groups, addressing limitations in existing models that fail to account for diperiodic materials like 2D superconductors and thin film semiconductors. The model combines discrete autoregressive lattice generation, transformer-based sampling, and equivariant diffusion, achieving superior performance on layered material discovery while correcting mathematical inconsistencies in prior diffusion approaches.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce ARMOR, an agentic framework that improves chemical reaction feasibility prediction by intelligently combining multiple AI tools rather than relying on single models. The system uses hierarchical tool organization and memory-augmented reasoning to resolve conflicting predictions, demonstrating significant performance gains especially when different tools disagree on outcomes.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers introduce MORetro*, a multi-objective optimization algorithm for computer-aided synthesis planning that generates Pareto-optimal routes balancing cost, sustainability, toxicity, and yield. This approach moves beyond single-route solutions to provide chemists with practical trade-off alternatives aligned with real-world industrial decision-making.
AIBullisharXiv – CS AI · Mar 37/107
🧠Researchers introduce DeMol, a new dual-graph framework for molecular property prediction that explicitly models both atoms and chemical bonds to achieve superior accuracy. The approach addresses limitations of conventional atom-centric models by incorporating bond-level phenomena like resonance and stereoselectivity, establishing new state-of-the-art results across multiple benchmarks.
$ATOM
AINeutralarXiv – CS AI · Jun 234/10
🧠QBioFusion-QSAR introduces a quantum multiple kernel learning framework combining quantum fidelity kernels with traditional Morgan fingerprints for drug discovery classification tasks. On a 54-molecule benchmark, the hybrid approach modestly improved accuracy and correlation metrics, though statistical validation across multiple random partitions showed gains were not consistently significant beyond classical methods.
