Abstract Motivation This paper addresses the challenge of incorporating 3-dimensional (3D) structural information in graph kernels for machine learning-based virtual screening, a crucial task in drug discovery. Existing kernels that capture 3D information often suffer from high computational complexity, which limits their scalability. Results To overcome this, we propose the 3D chain motif graph kernel, which effectively integrates essential 3D structural properties—bond length, bond angle, and torsion angle—within the three-hop neighborhood of each atom in a molecule. In addition, we introduce a more computationally efficient variant, the 3D graph hopper kernel (3DGHK), which reduces the complexity from the state-of-the-art O(n6) (for the 3D pharmacophore kernel) to O(n2(m+log(n)+δ2+dT6)). Here, n is the number of nodes, T is the highest degree of the node, m is the number of edges, δ is the diameter of the graph, and d is the dimension of the attributes of the nodes. We conducted experiments on 21 datasets, demonstrating that 3DGHK outperforms other state-of-the-art 2D and 3D graph kernels, but it also surpasses deep learning models in classification accuracy, offering a powerful and scalable solution for virtual screening tasks. Availability and implementation Our code is publicly available at https://github.com/SantaAnkit/Efficient-3D-kernels-for-molecular-property-prediction.git.

With almost half the Indian population facing water stress [ 42 ], likely to be aggravated with climate change [ 76 , 122 ], an intersecting crises of environment, livelihood, and social justice can worsen the vulnerability of rural communities that are directly dependent upon water resources for their livelihood and sustenance. To address this challenge of rural water security especially for marginalized communities, MGNREGA is a public works and employment generation scheme in India that contributes towards the construction of water related assets such as farm ponds, checkdams, and trenches and bunds, to improve irrigation and climate resilience of rural communities. MGNREGA is a demand-driven scheme that solicits demands for water related assets through the local governance structure in India of village-level community meetings called Gram Sabhas. In many areas, Civil Society Organizations (CSOs) and community based institutions also play a significant role by coordinating participatory processes to build the capacity of rural community members to participate in local governance and demand such assets under MGNREGA. However, through multiple field visits, we observed a need to strengthen local processes with appropriate data and tools to support context-sensitive and evidence-based bottom-up planning. In this study, we adopt an approach of building a digital participatory tool, built on a technology stack that encapsulates different geospatial datasets, to answer key research questions of whether such technology artefacts can improve the participation of rural communities in water security planning and management, through, (1) community empowerment by building a data-driven understanding of water security, (2) ecological sustainability of water resources through scientific planning of assets, and (3) equity led participatory processes to improve fairness in the distribution of assets. We discuss here the design of the tool, Commons Connect, underlying datasets on which it is built, and findings from an initial field testing of the tool across four locations in India. We outline several improvements and open questions that remain, and highlight how such tools can be integrated into the MGNREGA operational workflow and improve the sustainability and resilience of rural social-ecological systems.

Predicting the motion of observed entities benefits humans almost seamlessly. The same benefits can be proliferated to mobile autonomous systems if we have a reliable, real-time solution to predict the motion of any object of interest, be it the host’s own motion or that of an observed foreign object. In this work, a novel Multi-Variable State Prediction (MVSP) methodology is devised for real-time trajectory prediction. MVSP incorporates cascaded stages of HMM with Viterbi algorithm and probabilistic quantization for accurately predicting the motion characteristics of the moving object. The overall scheme is employed to predict the motion of moving objects in a 3D space. The proposed approach is verified on both synthetically generated data sequences and data-sets captured from real-life experiments. For a practical scenario, the experiments resulted in an RMS error of 0.6m for a predicted distance of ~18m demonstrating the effectiveness and accuracy of the proposed methodology.