2021-present

A novel framework based on explainable AI and genetic algorithms for designing neurological medicines (Nature Scientific Reports)

Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences. 


Vishakha Singh, et al. "A novel framework based on explainable AI and genetic algorithms for designing neurological medicines" in Nature Sci Rep 14, 12807 (2024). https://doi.org/10.1038/s41598-024-63561-3 

Designing new blood-brain barrier penetrating molecules using novel hybridized gravitational search algorithm and explainable AI (IEEE Transactions on Artificial Intelligence)

Artificial intelligence has emerged as a powerful tool in computational biology, where it is being used to analyze large datasets to detect difficult biological patterns. This has enabled the design of new drug molecules. In this paper, a novel method called the hybridized gravitational search algorithm (HyGSA) has been proposed to design novel blood-brain barrier penetrating peptides (B3P2s) with desirable characteristics that enable them to cross the blood-brain barrier (BBB) and deliver neurological drugs directly to the brain. The HyGSA has two important modules in the form of an explainable machine learning classifier and an explainable deep learning-based B3P2 classifier. The former was used to determine the crucial hand-engineered features, and the latter was designed to determine the critical amino acids that play an important role in the BBB penetrability of a peptide. Lastly, a free online tool has been deployed at https://b3p2design.anvil.app to help the scientific community discover and optimize B3P2s in protein sequences. 


Vishakha Singh, et al. "Designing New Blood-Brain Barrier Penetrating Molecules Using Novel Hybridized Gravitational Search Algorithm and Explainable AI," in IEEE Transactions on Artificial Intelligence, doi: 10.1109/TAI.2023.3313130. 

A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines (Nature Scientific Reports)

An alarming number of fatalities caused by the COVID-19 pandemic has forced the scientific community to accelerate the process of therapeutic drug discovery. In this regard, the collaboration between biomedical scientists and experts in artificial intelligence (AI) has led to a number of in silico tools being developed for the initial screening of therapeutic molecules. All living organisms produce antiviral peptides (AVPs) as a part of their first line of defense against invading viruses. The Deep-AVPiden model proposed in this paper and its corresponding web app, deployed at https://deep-avpiden.anvil.app, is an effort toward discovering novel AVPs in proteomes of living organisms. Apart from Deep-AVPiden, a computationally efficient model called Deep-AVPiden (DS) has also been developed using the same underlying network but with point-wise separable convolutions. The Deep-AVPiden and Deep-AVPiden (DS) models show an accuracy of 90% and 88%, respectively, and both have a precision of 90%. Also, the proposed models were statistically compared using the Student’s t-test. On comparing the proposed models with the state-of-the-art classifiers, it was found that they are much better than them. To test the proposed model, we identified some AVPs in the natural defense proteins of plants, mammals, and fishes and found them to have appreciable sequence similarity with some experimentally validated antimicrobial peptides. These AVPs can be chemically synthesized and tested for their antiviral activity. 


 Vishakha Singh, et al. A separable temporal convolutional networks based deep learning technique for discovering antiviral medicines. Nature Sci Rep 13, 13722 (2023). https://doi.org/10.1038/s41598-023-40922-y 

The high incidence of diseases caused by multi-drug resistant (MDR) pathogens combined with the shortage of effective antibiotics has necessitated the development of in-silico machine and deep learning tools to facilitate rapid drug discovery. The construction of computational models to discover antibacterial peptides (ABPs) in proteins of various organisms to develop a new line of antibiotics has emerged as a possible recourse. To this end, we used multi-scale temporal convolutional networks (MSTCN) to develop a robust deep learning-based model called MSTCN-ABPpred (BL) that classifies ABPs with an accuracy of 98% (which is better than various state-of-the-art models). The main contribution of this proposed work is that we have incorporated a continual learning module in this model so that it keeps adapting itself dynamically by re-training on new data points. This re-trainable version of the baseline model (MSTCN-ABPpred (BL)) was termed as MSTCN-ABPpred (CL). We re-trained this model on the ABPs and non-ABPs predicted by it in some antibacterial proteins. It has been demonstrated that the proposed model does not exhibit any statistically significant deterioration in performance after extensive re-training, and it gains additional skills compared to the MSTCN-ABPpred (BL). We have also deployed a freely accessible web application based on our final model, available at https://mstcn-abppred.anvil.app/, which can identify and discover ABPs in a protein using which the model gets re-trained on its own. 

Vishakha Singh, et al. "Multi-scale temporal convolutional networks and continual learning based in silico discovery of alternative antibiotics to combat multi-drug resistance." Expert Systems with Applications 215 (2023): 119295. 

Due to the rapid emergence of multi-drug resistant (MDR) bacteria, existing antibiotics are becoming ineffective. So, researchers are looking for alternatives in the form of antibacterial peptides (ABPs) based medicines. The discovery of novel ABPs using wet-lab experiments is time-consuming and expensive. In this work, we present StaBle-ABPpred, a stacked ensemble technique-based deep learning classifier that uses bidirectional long-short term memory (biLSTM) and attention mechanism at base-level and an ensemble of random forest, gradient boosting, and logistic regression at meta-level to classify peptides as antibacterial or otherwise.  

Vishakha Singh, et al. "StaBle-ABPpred: a stacked ensemble predictor based on biLSTM and attention mechanism for accelerated discovery of antibacterial peptides." Briefings in Bioinformatics 23, no. 1 (2022): bbab439. 

The increased application of machine intelligence in biological sciences has led to the development of several automated tools and techniques, thus enabling rapid drug discovery. TCN-AFPpred is one such attempt to predict and identify antifungal peptides in protein sequences of various organisms using Temporal Convolutional Networks (TCN) in order to accelerate the process of drug discovery against fungi that cause damage to plant, animal, and human health. 

Vishakha Singh, et al."Accelerating the discovery of antifungal peptides using deep temporal convolutional networks." Briefings in Bioinformatics 23, no. 2 (2022). 

2018-2020

Workflow scheduling is a crucial aspect of cloud computing that should be performed in an efficient manner for optimal utilization of resources. The development of a cost-efficient algorithm has always been an important topic of research in this regard. In this paper, we propose a novel workflow scheduling algorithm, which is cost-efficient and deadline-constrained. The proposed algorithm is consolidated by dynamic provisioning of the resources, using k-means clustering technique and a variant of the Subset-Sum problem. In the algorithm, we consider level based scheduling using the concept of Bag of Tasks (bots) and develop a new technique for associating deadlines with each bot. 

Vishakha Singh, et al. "A novel cost-efficient approach for deadline-constrained workflow scheduling by dynamic provisioning of resources." Future Generation Computer Systems 79 (2018): 95-110. 

Workflow Scheduling in cloud computing has drawn enormous attention due to its wide application in both scientific and business areas. This is particularly an NP-complete problem. Therefore, many researchers have proposed a number of heuristics as well as meta-heuristic techniques by considering several issues, such as energy conservation, cost and makespan. However, it is still an open area of research as most of the heuristics or meta-heuristics may not fulfill certain optimum criterion and produce near optimal solution. In this paper, we propose a meta-heuristic based algorithm for workflow scheduling that considers minimization of makespan and cost. The proposed algorithm is a hybridization of the popular meta-heuristic, Gravitational Search Algorithm (GSA) and equally popular heuristic, Heterogeneous Earliest Finish Time (HEFT) to schedule workflow applications. 

Choudhary, Anubhav, Indrajeet Gupta, Vishakha Singh, and Prasanta K. Jana. "A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing." Future Generation Computer Systems 83 (2018): 14-26. 

Energy efficient workflow scheduling is the demand of the present time’s computing platforms such as an infrastructure-as-a-service (IaaS) cloud. An appreciable amount of energy can be saved if a dynamic voltage scaling (DVS) enabled environment is considered. But it is important to decrease makespan of a schedule as well, so that it may not extend beyond the deadline specified by the cloud user. In this paper, we propose a workflow scheduling algorithm which is inspired from hybrid chemical reaction optimization (HCRO) algorithm. 

Vishakha Singh, et al. "An energy efficient algorithm for workflow scheduling in IAAS cloud." Journal of Grid Computing 18, no. 3 (2020): 357-376.

Data collection through mobile sink (MS) in wireless sensor networks (WSNs) is an effective solution to the hot-spot or sink-hole problem caused by multi-hop routing using the static sink. Rendezvous point (RP) based MS path design is a common and popular technique used in this regard. However, design of the optimal path is a well-known NP-hard problem. Therefore, an evolutionary approach like multi-objective particle swarm optimization (MOPSO) can prove to be a very promising and reasonable approach to solve the same. In this paper, we first present a Linear Programming formulation for the stated problem and then, propose an MOPSO-based algorithm to design an energy efficient trajectory for the MS.  

Kaswan, Amar, Vishakha Singh, and Prasanta K. Jana. "A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks." Pervasive and Mobile Computing 46 (2018): 122-136.