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Arijit Roy

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3 papers

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AAAI Conference 2024 Conference Paper

Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback

Shreyas Bhat Brahmavar
Ashwin Srinivasan
Tirtharaj Dash
Sowmya Ramaswamy Krishnan
Lovekesh Vig
Arijit Roy
Raviprasad Aduri

Large Language Models (LLMs) can be used as repositories of biological and chemical information to generate pharmacological lead compounds. However, for LLMs to focus on specific drug targets typically requires experimentation with progressively more refined prompts. Results thus become dependent not just on what is known about the target, but also on what is known about the prompt- engineering. In this paper, we separate the prompt into domain-constraints that can be written in a standard logical form and a simple text-based query. We investigate whether LLMs can be guided, not by refining prompts manually, but by refining the logical component automatically, keeping the query unchanged. We describe an iterative procedure LMLF (“Language Model with Logical Feedback”) in which the constraints are progressively refined using a logical notion of generalisation. On any iteration, newly generated instances are verified against the constraint, providing "logical-feedback" for the next iteration's refinement of the constraints. We evaluate LMLF using two well-known targets (inhibition of the Janus Kinase 2; and Dopamine Receptor D2); and two different LLMs (GPT-3 and PaLM). We show that LMLF, starting with the same logical constraints and query text, can be used to guide both LLMs to generate potential leads. We find: (a) Binding affinities of LMLF-generated molecules are skewed towards higher binding affinities than those from existing baselines; (b) LMLF results in generating molecules that are skewed towards higher binding affinities than without logical feedback; (c) Assessment by a computational chemist suggests that LMLF generated compounds may be novel inhibitors. These findings suggest that LLMs with logical feedback may provide a mechanism for generating new leads without requiring the domain-specialist to acquire sophisticated skills in prompt-engineering.

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TAAS Journal 2017 Journal Article

Topology Control for Self-Adaptation in Wireless Sensor Networks with Temporary Connection Impairment

Arijit Roy
Sudip Misra
Pushpendu Kar
Ayan Mondal

In this work, the problem of topology control for self-adaptation in stationary Wireless Sensor Networks (WSNs) is revisited, specifically for the case of networks with a subset of nodes having temporary connection impairment between them. This study focuses on misbehaviors arising due to the presence of\enskip “dumb” nodes [Misra et al. 2014; Roy et al. 2014a, 2014b, 2014c; Kar and Misra 2015], which can sense its surroundings but cannot communicate with its neighbors due to shrinkage in its communication range by the environmental effects attributed to change in temperature, rainfall, and fog. However, a dumb node is expected to behave normally on the onset of favorable environmental conditions. Therefore, the presence of such dumb nodes in the network gives rise to impaired connectivity between a subset of nodes and, consequently, results in change in topology. Such phenomena are dynamic in nature and are thus distinct from the phenomena attributed to traditional isolation problems considered in stationary WSNs. Activation of all the sensor nodes simultaneously is not necessarily energy efficient and cost-effective. In order to maintain self-adaptivity of the network, two algorithms, named Connectivity Re-establishment in the presence of Dumb nodes ( CoRD ) and Connectivity Re-establishment in the presence of Dumb nodes Without Applying Constraints ( CoRDWAC ), are designed. The performance of these algorithms is evaluated through simulation-based experiments. Further, it is also observed that the performance of CoRD is better than the existing topology control protocols—LETC and A1—with respect to the number of nodes activated, overhead, and energy consumption.

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TAAS Journal 2016 Journal Article

Connectivity Reestablishment in Self-Organizing Sensor Networks with Dumb Nodes

Pushpendu Kar
Arijit Roy
Sudip Misra

In this work, we propose a scheme, named CoRAD, for the reestablishment of lost connectivity using sensor nodes with adjustable communication range in stationary wireless sensor networks (WSNs), when “dumb” behavior occurs some of the nodes. Due to the occurrence of such behavior, there may be temporary loss of connectivity between among the nodes. Such a phenomenon is different from the commonly known node isolation problem in stationary WSNs. The mere activation of intermediate sleep nodes cannot guarantee reestablishment of connectivity, because there may not exist neighbor nodes of the isolated nodes. On the contrary, the increase in communication range of a single sensor node may make it die quickly. Including this, a sensor node has maximum limit of increase in communication range that may not be sufficient to reestablish connectivity. Therefore, considering all these factors for self-organization of the network and isolated node re-connection, we propose a price-based scheme, which addresses the issue by activating intermediate sleep nodes or by adjusting the communication range of some of the other nodes in the network. The scheme also deactivates the additional activated nodes and reduces the increased communication range when the dumb nodes resume their normal behavior, upon the return of favorable environmental conditions. To implement the proposed scheme, CoRAD it is required to construct the network using GPS-enabled adjustable communication range sensor nodes. Through simulation we compare our proposed scheme with the existing topology management schemes -- LETC and A1 -- in the same scenario by considering the number of activated nodes, message overhead, and energy consumption. We find that the proposed scheme shows improved performance compared to the existing topology management schemes.

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