In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph Neural Network (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom's similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other GNNs in CADD. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. Our codes are publicly available at https://github.com/meilerlab/MolKGNN.

In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemical information such as molecular chirality is ignored. To fill this crucial gap, we propose Molecular-Kernel Graph NeuralNetwork (MolKGNN) for molecular representation learning, which features SE(3)-/conformation invariance, chirality-awareness, and interpretability. For our MolKGNN, we first design a molecular graph convolution to capture the chemical pattern by comparing the atom's similarity with the learnable molecular kernels. Furthermore, we propagate the similarity score to capture the higher-order chemical pattern. To assess the method, we conduct a comprehensive evaluation with nine well-curated datasets spanning numerous important drug targets that feature realistic high class imbalance and it demonstrates the superiority of MolKGNN over other graph neural networks in computer-aided drug discovery. Meanwhile, the learned kernels identify patterns that agree with domain knowledge, confirming the pragmatic interpretability of this approach. Our code and supplementary material are publicly available at https://github.com/meilerlab/MolKGNN.

In recent years several applications of graph neural networks (GNNs) to molecular tasks have emerged. Whether GNNs outperform the traditional descriptor-based methods in the quantitative structure activity relationship (QSAR) modeling in early computer-aided drug discovery (CADD) remains an open question. This paper introduces a simple yet effective strategy to boost the predictive power of QSAR deep learning models. The strategy proposes to train GNNs together with traditional descriptors, combining the strengths of both methods. The enhanced model consistently outperforms vanilla descriptors or GNN methods on nine well-curated high throughput screening datasets over diverse therapeutic targets.

In building a new drug design mode for the popular citizen scientist game Foldit, we focus on creating an easy-to-use and intuitive interface to confer complex scientific concepts to citizen scientist players. We hypothesize that to be efficient in the hands of citizen scientists such an interface will look different from well-established drug-design software used by experts. We used the relaxed thinkaloud method to compare citizen and expert scientists working with our prototype interface for Foldit Drug Design Mode (FDDM). First, we tested if the two groups are providing different feedback when it comes to the usability of the prototype interface. Second, we investigated how the difference between the two groups might inform a new game design. As expected, the results confirm that experienced scientists differ from citizen scientists in engaging their background knowledge when interacting with the game. We then provided a prioritization list of background knowledge employed by the expert scientists to derive design suggestions for FDDM. CCS CONCEPTS • Human-centered computing → Empirical studies in interaction design.