Garikapati Narahari Sastry

Predicting the partition coefficient (logP) is critical in pharmaceutical research for assessing the bioavailability of small molecules. We present a novel quantum physics-inspired machine learning model (QM-ML) for predicting logP and compare its performance with an empirical descriptor-based model and a graph neural network (GNN) model. For all the models investigated, feature selection was conducted to refine the high-dimensional descriptor space and identify critical determinants of lipophilicity or logP. Quantum mechanical descriptors, in particular frontier orbital energies derived, such as chemical potential, hardness, electrophilicity, electronegativity, and dipole moment, transfer energy, and quantum mechanical polar surface area (QMPSA), were extracted to construct QM-ML mod. The results indicate that the hybrid model achieved the highest predictive accuracy, yielding an R2 of 0.9248, a mean absolute error (MAE) of 0.3511, and a root mean square error (RMSE) of 0.4981. The ExtraTrees regressor, utilizing empirical classical Mordred descriptors, demonstrated comparable performance with an R2 of 0.9141. While models relying exclusively on frontier orbital energies showed limited accuracy (R2 = 0.4349), the inclusion of explicit solvation energy (ΔG) and QMPSA significantly improved the physics-based model performance (R2 = 0.8132). The DeepChem-based GCN model also achieved an R2 of 0.8936. Notably, the explainability analysis showed that certain QM descriptors, such as solvation energy (ΔG) and non-polar QMPSA, contributed significantly to the model performance. The hybrid model outperformed all the physics-based and baseline Mordred models with large-scale descriptor sets and showed the importance of certain QM descriptors, such as solvation transfer energy and QM-PSA. These findings highlight the potential of a hybrid data-driven model involving physics-based descriptors to capture complex molecular interactions and underscore the importance of targeted feature engineering in quantitative structure–property relationship modeling.

Linear B-cell epitopes are antigenic regions recognised by antibodies that play crucial roles in vaccine development, antibody production, and other therapeutic applications. Their prediction can support peptide-based vaccine candidate screening and diagnostic assay development, but the experimental data required for model development may be distributed across institutions that cannot freely pool their records. We present FedEpitope, a federated framework that adapts the ESM-2 protein language model for peptide-level linear B-cell epitope prediction using Low-Rank Adaptation (LoRA), keeping client-partitioned data local and restricting the communicated trainable model state to approximately 1.11 MB. Simulated benchmarks of FedAvg and FedProx algorithms, evaluated using homology-aware data splits of five biologically heterogeneous, organism-partitioned clients, achieve promising global test performance without centralising training data, with FedAvg outperforming all single-client models in mean AP, F1, and MCC. Additional experiments characterise post-federation personalisation, record-level differential privacy, sensitivity to controlled client heterogeneity, LoRA rank, and generalisation to the independent BCPreds and NetBCE benchmarks, demonstrating the feasibility of parameter-efficient federated protein language model adaptation for data-localised B-cell epitope prediction and the associated trade-offs among predictive performance, personalisation, privacy, communication efficiency, and distribution shift. VISUAL

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development. Availability & Implementation The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers are actively involved in the development of innovative models and strategies for material design. Recent advancements in analytical tools, experimentation, and computer technology additionally enhance the material design possibilities. Notably, data-driven techniques like artificial intelligence and machine learning have achieved substantial progress in exploring various applications within material science. One such approach, ChatGPT, a large language model, holds transformative potential for addressing complex queries. In this article, we explore ChatGPT's understanding of material science by assigning some simple tasks across various subareas of computational material science. The findings indicate that while ChatGPT may make some minor errors in accomplishing general tasks, it demonstrates the capability to learn and adapt through human interactions. However, issues like output consistency, probable hidden errors, and ethical consequences should be addressed.

Molecular Property Diagnostic Suite Compound Library (MPDS-CL) is an open-source Galaxy-based cheminformatics web portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, obtained from 42 publicly available databases and curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions, has been done. These are further subjected to 56-bit fingerprint-based classification algorithm which led to the formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put into 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-AadharID, for each of these 149,169,443 molecules. MPDS-AadharID is akin to the unique number given to citizens in India (similar to SSN in the US and NINO in the UK). The unique features of MPDS-CL are (a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; (b) automatic generation of information for the processing for MPDS and other galaxy tools; (c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and (d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at https://mpds.neist.res.in:8086/. Graphical abstract

The cation‐aromatic database (CAD) is a comprehensive repository of cation‐aromatic motifs found in experimentally determined protein structures, first reported in 2007 [Proteins, 2007, 67, 1179]. The present article is an update of CAD that contains information of approximately 27.26 million cation‐aromatic motifs. CAD uses three distance parameters (r, d1, and d2) to determine the position of the cation relative to the centroid of the aromatic residue and classifies the motifs as cation‐π or cation‐σ interactions. As of June 2023, about 193 936 protein structures were retrieved from Protein Data Bank, and this resulted in the identification of an impressive number of 27 255 817 cation‐aromatic motifs. Among these motifs, spherical motifs constituted 94.09%, while cylindrical motifs made up the remaining 5.91%. When considering the interaction of metal ions with aromatic residues, 965 564 motifs are identified. Remarkably, 82.08% of these motifs involved the binding of metal ions to the amino acid HIS. Moreover, the analysis of binding preferences between cations and aromatic residues revealed that the HIS‐HIS, PHE‐ARG, and TRP‐ARG pairs exhibited a preferential geometry. The motif pair HIS‐HIS was the most prevalent, accounting for 19.87% of the total, closely followed by TYR‐LYS at 10.17%. Conversely, the motif pair TRP‐HIS had the lowest occurrence, representing only 4.20% of the total. The data generated help in revealing the characteristics and biological functions of cation‐aromatic interactions in biological molecules. The updated version of CAD (Cation‐Aromatic Database V2.0) can be accessed at https://acds.neist.res.in/cadv2.

Computational drug discovery is intrinsically interdisciplinary and has to deal with the multifarious factors which are often dependent on the type of disease. Molecular Property Diagnostic Suite (MPDS) is a Galaxy based web portal which was conceived and developed as a disease specific web portal, originally developed for tuberculosis (MPDS ^TB ). As specific computational tools are often required for a given disease, developing a disease specific web portal is highly desirable. This paper emphasises on the development of the customised web portal for COVID-19 infection and is referred to as MPDS ^COVID-19 . Expectedly, the MPDS suites of programs have modules which are essentially independent of a given disease, whereas some modules are specific to a particular disease. In the MPDS ^COVID-19 portal, there are modules which are specific to COVID-19, and these are clubbed in SARS-COV-2 disease library. Further, the new additions and/or significant improvements were made to the disease independent modules, besides the addition of tools from galaxy toolshed. This manuscript provides a latest update on the disease independent modules of MPDS after almost 6 years, as well as provide the contemporary information and tool-shed necessary to engage in the drug discovery research of COVID-19. The disease independent modules include file format converter and descriptor calculation under the data processing module; QSAR, pharmacophore, scaffold analysis, active site analysis, docking, screening, drug repurposing tool, virtual screening, visualisation, sequence alignment, phylogenetic analysis under the data analysis module; and various machine learning packages, algorithms and in-house developed machine learning antiviral prediction model are available. The MPDS suite of programs are expected to bring a paradigm shift in computational drug discovery, especially in the academic community, guided through a transparent and open innovation approach. The MPDS ^COVID-19 can be accessed at http://mpds.neist.res.in:8085 .

Molecular Property Diagnostic Suite-Compound Library (MPDS-CL), is an open-source galaxy-based cheminformatics web-portal which presents a structure-based classification of the molecules. A structure-based classification of nearly 150 million unique compounds, which are obtained from 42 publicly available databases were curated for redundancy removal through 97 hierarchically well-defined atom composition-based portions. These are further subjected to 56-bit fingerprint-based classification algorithm which led to a formation of 56 structurally well-defined classes. The classes thus obtained were further divided into clusters based on their molecular weight. Thus, the entire set of molecules was put in 56 different classes and 625 clusters. This led to the assignment of a unique ID, named as MPDS-Aadhar card, for each of these 149 169 443 molecules. Aadhar card is akin to the unique number given to citizens in India (similar to the SSN in US, NINO in UK). MPDS-CL unique features are: a) several search options, such as exact structure search, substructure search, property-based search, fingerprint-based search, using SMILES, InChIKey and key-in; b) automatic generation of information for the processing for MPDS and other galaxy tools; c) providing the class and cluster of a molecule which makes it easier and fast to search for similar molecules and d) information related to the presence of the molecules in multiple databases. The MPDS-CL can be accessed at http://mpds.neist.res.in:8086/.

Drug repurposing has emerged as an important strategy and it has a great potential in identifying therapeutic applications for COVID-19. An extensive virtual screening of 4193 FDA approved drugs has been carried out against 24 proteins of SARS-CoV2 (NSP1-10 and NSP12-16, envelope, membrane, nucleoprotein, spike, ORF3a, ORF6, ORF7a, ORF8, and ORF9b). The drugs were classified into top 10 and bottom 10 drugs based on the docking scores followed by the distribution of their therapeutic indications. As a result, the top 10 drugs were found to have therapeutic indications for cancer, pain, neurological disorders, and viral and bacterial diseases. As drug resistance is one of the major challenges in antiviral drug discovery, polypharmacology and network pharmacology approaches were employed in the study to identify drugs interacting with multiple targets and drugs such as dihydroergotamine, ergotamine, bisdequalinium chloride, midostaurin, temoporfin, tirilazad, and venetoclax were identified among the multi-targeting drugs. Further, a pathway analysis of the genes related to the multi-targeting drugs was carried which provides insight into the mechanism of drugs and identifying targetable genes and biological pathways involved in SARS-CoV2.