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1. Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique

   https://doi.org/10.21203/rs.3.rs-4355625/v1  

   Monsia, Rivaaj; Bhattacharyya, Sudeep (May 2024, Research Square)

   Abstract A machine learning-based drug screening technique has been developed and optimized using convolutional neural network-derived fingerprints. The optimization of weights in the neural network-based fingerprinting technique was compared with fixed Morgan fingerprints in regard to binary classification on drug-target binding affinity. The assessment was carried out using six different target proteins using randomly chosen small molecules from the ZINC15 database for training. This new architecture proved to be more efficient in screening molecules that less favorably bind to specific targets and retaining molecules that favorably bind to it. Scientific contribution We have developed a new neural fingerprint-based screening model that has a significant ability to capture hits. Despite using a smaller dataset, this model is capable of mapping chemical space similar to other contemporary algorithms designed for molecular screening. The novelty of the present algorithm lies in the speed with which the models are trained and tuned before testing its predictive capabilities and hence is a significant step forward in the field of machine learning-embedded computational drug discovery. 

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2. Leveraging conformal prediction to annotate enzyme function space with limited false positives

   https://doi.org/10.1371/journal.pcbi.1012135  

   Ding, Kerr; Luo, Jiaqi; Luo, Yunan (May 2024, PLOS Computational Biology)

   Mura, Cameron (Ed.)

   Machine learning (ML) is increasingly being used to guide biological discovery in biomedicine such as prioritizing promising small molecules in drug discovery. In those applications, ML models are used to predict the properties of biological systems, and researchers use these predictions to prioritize candidates as new biological hypotheses for downstream experimental validations. However, when applied to unseen situations, these models can be overconfident and produce a large number of false positives. One solution to address this issue is to quantify the model’s prediction uncertainty and provide a set of hypotheses with a controlled false discovery rate (FDR) pre-specified by researchers. We propose CPEC, an ML framework for FDR-controlled biological discovery. We demonstrate its effectiveness using enzyme function annotation as a case study, simulating the discovery process of identifying the functions of less-characterized enzymes. CPEC integrates a deep learning model with a statistical tool known as conformal prediction, providing accurate and FDR-controlled function predictions for a given protein enzyme. Conformal prediction provides rigorous statistical guarantees to the predictive model and ensures that the expected FDR will not exceed a user-specified level with high probability. Evaluation experiments show that CPEC achieves reliable FDR control, better or comparable prediction performance at a lower FDR than existing methods, and accurate predictions for enzymes under-represented in the training data. We expect CPEC to be a useful tool for biological discovery applications where a high yield rate in validation experiments is desired but the experimental budget is limited. 

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3. Siamese Graph Neural Networks for Melting Temperature Prediction of Molten Salt Eutectics

   https://doi.org/10.26434/chemrxiv-2025-l7gf8  

   Mandal, Nila; Maniscalco, James; Aindow, Mark; Yang, Qian (November 2025, ChemRxiv)

   High-throughput screening enabled by structure-property prediction models is a powerful approach for accelerating materials discovery. However, while machine learning of structure-property models have become widespread, its application to mixtures remains limited due to increased complexity and the scarcity of available data. Machine learning methods for high-throughput screening of eutectic mixtures have been proposed in recent years, but there remain challenges due to the lack of diverse, open-access datasets and the need for feature engineering based on chemical knowledge. To overcome these limitations, we propose a method using Siamese graph neural networks trained solely on structural information, without requiring any prior chemical descriptors, to predict eutectic melting temperatures. We demonstrate on a dataset of molten salt eutectics that this approach can reach similar performance to chemistry-based models that require significantly more prior knowledge. We show that lower-order mixtures may be used to augment data on higher-order mixtures. Interestingly, our model trained on inorganic molten salts seems to learn information about the ideal mixture model. We also evaluate the efficacy of using our inorganic molten salt model for transfer learning with a variety of organic eutectic mixtures. 

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4. Machine Learning Methods in Drug Discovery

   https://doi.org/10.3390/molecules25225277  

   Patel, Lauv; Shukla, Tripti; Huang, Xiuzhen; Ussery, David W.; Wang, Shanzhi (November 2020, Molecules)

   null (Ed.)

   The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed. 

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5. BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies

   https://doi.org/10.1186/s12859-023-05316-x  

   Williams, Jacob; Xu, Shuangshuang; Ferreira, Marco A. (December 2023, BMC Bioinformatics)

   Abstract Background Genome-wide association studies (GWAS) seek to identify single nucleotide polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated SNPs, correlated observations, and the number of SNPs being two orders of magnitude larger than the number of observations, GWAS procedures often suffer from high false positive rates. Results We propose BGWAS, a novel Bayesian variable selection method based on nonlocal priors for linear mixed models specifically tailored for genome-wide association studies. Our proposed method BGWAS uses a novel nonlocal prior for linear mixed models (LMMs). BGWAS has two steps: screening and model selection. The screening step scans through all the SNPs fitting one LMM for each SNP and then uses Bayesian false discovery control to select a set of candidate SNPs. After that, a model selection step searches through the space of LMMs that may have any number of SNPs from the candidate set. A simulation study shows that, when compared to popular GWAS procedures, BGWAS greatly reduces false positives while maintaining the same ability to detect true positive SNPs. We show the utility and flexibility of BGWAS with two case studies: a case study on salt stress in plants, and a case study on alcohol use disorder. Conclusions BGWAS maintains and in some cases increases the recall of true SNPs while drastically lowering the number of false positives compared to popular SMA procedures. 

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