An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility
arXiv:2607.02212v1 Announce Type: cross Abstract: Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information into a single representation, obscuring whether a prediction is driven by global chemistry, molecular structure, or both. We present an additive deep-learning framework that keeps these two sources of information separate throughout training: physicochemical descriptors are encoded by a multilayer perceptron (the chemical branch) and molecular graph topology by a graph neural network (t
The increasing sophistication of AI models, particularly in chemistry and biology, is enabling more precise and interpretable predictions in drug discovery.
This framework offers a more transparent and dissectible approach to predicting aqueous solubility, which is critical for efficient and cost-effective drug development.
Drug discovery processes can become more targeted, allowing researchers to understand whether molecular structure or chemical properties are driving solubility and optimize accordingly.
- · Pharmaceutical companies
- · Synthetic biology researchers
- · AI-driven drug discovery platforms
- · Computational chemists
- · Traditional high-throughput screening methods
- · Drug discovery models lacking interpretability
Improved prediction accuracy and interpretability for drug potency, toxicity, and synthesis will accelerate therapeutic development cycles.
Faster and cheaper drug development could lead to a broader range of available treatments for various diseases.
The enhanced efficiency in molecular design could accelerate advancements in material science and other chemistry-dependent industries.
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Read at arXiv cs.LG