#molecular-design News & Analysis

7 articles tagged with #molecular-design. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles

AIBullisharXiv – CS AI · May 287/10

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MolLingo: Molecule-Native Representations for LLM-Powered Scientific Agents

Researchers introduce MolLingo, a multi-agent AI system that automates molecular design by coordinating specialized agents through shared memory and domain-specific tools. The system uses BRICS-based Fragment Enumeration to represent molecules in chemically meaningful ways that LLMs can reason about effectively, achieving superior performance on drug design benchmarks compared to frontier models like GPT-5.

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AIBullisharXiv – CS AI · Mar 117/10

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Logos: An evolvable reasoning engine for rational molecular design

Researchers introduce Logos, a compact AI model that combines multi-step logical reasoning with chemical consistency for molecular design. The model achieves strong performance in structural accuracy and chemical validity while using fewer parameters than larger language models, and provides transparent reasoning that can be inspected by humans.

AIBullisharXiv – CS AI · Mar 37/103

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mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules

Researchers developed mCLM, a 3-billion parameter modular Chemical Language Model that generates functional molecules compatible with automated synthesis by tokenizing at the building block level rather than individual atoms. The AI system outperformed larger models including GPT-5 in creating synthesizable drug candidates and can iteratively improve failed clinical trial compounds.

AINeutralarXiv – CS AI · Jun 46/10

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Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?

Researchers introduce ToxiMol, the first benchmark dataset and evaluation framework for assessing Multimodal Large Language Models (MLLMs) on molecular toxicity repair—the task of generating structurally valid alternatives to toxic compounds. Testing 43 mainstream MLLMs reveals current models show promise in toxicity understanding and constraint adherence but face significant challenges in this specialized pharmaceutical application.

AINeutralarXiv – CS AI · Jun 26/10

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Evidence-Gated LLM Priors for Multi-Objective Bayesian Optimization

Researchers propose a framework for incorporating Large Language Model (LLM) priors into multi-objective Bayesian optimization while maintaining robustness against miscalibrated advice. Using an objective-wise reputation mechanism and counterfactual gating, the approach dynamically adjusts trust in LLM suggestions based on observed performance rather than accepting them blindly, with empirical validation across molecular optimization tasks.

AINeutralarXiv – CS AI · May 126/10

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Primal-Dual Guided Decoding for Constrained Discrete Diffusion

Researchers introduce primal-dual guided decoding, an inference-time method for discrete diffusion models that enforces global constraints during token generation through adaptive Lagrangian multipliers and KL-regularized optimization. The approach requires no model retraining, supports multiple simultaneous constraints, and demonstrates effectiveness across text generation, molecular design, and music applications.

AINeutralarXiv – CS AI · May 116/10

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Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science

Researchers introduce CFM-SD, a causal discovery method that leverages physical simulators to identify cause-and-effect relationships in scientific domains while handling latent confounders—a common problem in molecular design and materials science. The approach achieves significantly higher accuracy than existing methods and demonstrates practical improvements in real-world applications like toxicity prediction and battery optimization.