Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET—absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

Chagas disease and Leishmaniasis are neglected endemic protozoan diseases recognized as public health problems by the World Health Organization. These diseases affect millions of people around the world however, efficient and low-cost treatments are not available. Different steroid molecules with antimicrobial and antiparasitic activity were isolated from diverse organisms (ticks, plants, fungi). These molecules have complex structures that make de novo synthesis extremely difficult. In this work, we designed new and simpler compounds with antiparasitic potential inspired in natural steroids and synthesized a series of nineteen steroidal arylideneketones and thiazolidenehydrazines. We explored their biological activity against Leishmania infantum, Leishmania amazonensis, and Trypanosoma cruzi in vitro and in vivo. We also assayed their genotoxicity and acute toxicity in vitro and in mice. The best compound, a steroidal thiosemicarbazone compound 8 (ID_1260) was active in vitro (IC₅₀ 200 nM) and in vivo (60% infection reduction at 50 mg/kg) in Leishmania and T. cruzi. It also has low toxicity in vitro and in vivo (LD₅₀ >2000 mg/kg) and no genotoxic effects, being a promising compound for anti-trypanosomatid drug development.

The rapid emergence of drug resistance in viral infections represents a significant global health challenge, threatening the efficacy of treatments for multiple diseases. Machine learning models have emerged as valuable tools for predicting antiviral drug resistance from genomic data, with HIV-1 protease serving as a well-characterized model system due to its extensive experimental data and clinical relevance. Here, we systematically evaluate multiple previously published HIV-1 protease inhibitor (PI) resistance prediction models across three distinct data sets with different preprocessing and ambiguous sequencing processing strategies and propose a new approach for preprocessing. We tested Steiner's data set (n = 1540) with first-amino-acid selection at ambiguous positions, Shen's expanded data set (n = 500,390) with all possible combinations at ambiguous positions, and our In-house data set (n = 869) with strict exclusion of ambiguous sequences. We compare neural networks architectures (Multilayer Perceptron, Bidirectional Recurrent Neural Network, and Convolutional Neural Network), traditional machine learning models (Random Forest and K-Nearest Neighbor), and logistic regression using either zScales physicochemical descriptors or Rosetta energy terms. Sequence expansion preprocessing can artificially increase performance metrics (mean AUC: 0.986-0.999) by creating substantial redundancy (99.6% of expanded data set consists of duplicated sequences from 2096 unique originals), while our clustering-based validation approach provides a more stringent assessment of model generalizability. Remarkably, our physicochemically informed logistic regression models achieved performance comparable to complex neural networks on challenging test sets (zScales LR: AUC = 0.973; Rosetta LR: AUC = 0.944), while offering superior interpretability. Furthermore, the zScales LR model offered significantly greater computational efficiency (0.007 s/prediction) compared to that of Rosetta LR (776.117 s/prediction). Mutual information analysis revealed distinct complementary resistance mechanisms: The zScales descriptors identified discrete resistance hotspots at positions 10, 46, 54, 71, and 90, while the Rosetta energy terms revealed interconnected energetic networks across structurally adjacent residues, particularly in functionally critical flap regions (positions 46-54). This study demonstrates how data set construction choices directly impact apparent model performance while establishing that well-chosen physicochemical feature representations can match or exceed complex neural networks for HIV-1 PI resistance modeling, offering both accuracy and mechanistic interpretability critical for clinical implementation and drug development.

The T-cell receptor (TCR) recognition of multiple peptides presented by the major histocompatibility complex (MHC) is a key natural phenomenon, enabling the T-cell repertoire to respond to a broad array of antigens. Despite its importance to the immune response, T-cell cross-reactivity poses a major challenge for the development of novel T cell–based therapies. In this study, we present MHCXGraph, a graph-based computational approach for identifying conserved and immunologically relevant regions across multiple structures of peptides bound to MHC molecules (pMHC). Our approach provides three operational modes: Multiple, Pairwise, and Screening, with user-defined parameters, allowing flexible configuration according to specific scientific needs while delivering fully interpretable results through user-friendly interfaces. We evaluated MHCXGraph across several case studies, including peptides bound to classical MHC Class I, MHC Class II, unbound human leukocyte antigen (HLA) alleles, and pMHCs containing noncanonical amino acids, demonstrating its ability to capture conserved structural determinants beyond sequence similarity. By integrating structural information with efficient graph-based analysis, MHCXGraph addresses key limitations of sequence-based methods while maintaining computational scalability. Collectively, these results indicate that MHCXGraph can be readily integrated into computational pipelines for T-cell cross-reactivity discovery, especially in the context of $\textit{de novo}$ pMHC engager design and T cell–based vaccine development.

A bstract: The T cell receptor (TCR) recognition of multiple peptides presented by the major histocompatibility complex (MHC) is a key natural phenomenon, enabling the T cell repertoire to respond to a broad array of antigens. Despite its importance to the immune response, T cell cross-reactivity poses a major challenge for the development of novel T cell–based therapies. In this study, we present MHCXGraph , a graph-based computational approach for identifying conserved and immunologically relevant regions across multiple structures of peptides bound to MHC molecules (pMHC). Our approach provides three operational modes with user-defined parameters, allowing flexible configuration according to specific scientific needs while delivering fully interpretable results through user-friendly interfaces. We evaluated MHCXGraph across three case studies, including peptides bound to classical MHC Class I, MHC Class II, and unbound HLA alleles, demonstrating its ability to capture conserved structural determinants beyond sequence similarity. By integrating structural information with efficient graph-based analysis, MHCXGraph addresses key limitations of sequence-based methods while maintaining computational scalability. Collectively, these results indicate that MHCXGraph can be readily integrated into computational pipelines for T cell cross-reactivity discovery, especially in the context of de novo pMHC engager design and T cell–based vaccine development.

En Paraguay, así como en las Américas, la leishmaniasis constituye una de las problemáticas de salud pública importante, debido a su complejidad tanto epidemiológica, clínica y biológica, afectando especialmente a los más pobres y en los países en vía de desarrollo. Polygonum punctatum Elliot. pertenece a la familia Polygonaceae, la medicina popular le atribuye varias propiedades, por medio de estudios anteriores se confirma que diferentes extractos de esta planta presentan actividad antibacteriana, antiinflamatoria y antifúngica. En este trabajo, se evaluó la actividad citotóxica del extracto etanólico de Polygonum punctatum de la familia Polygonaceae en concentraciones de 100, 50 y 25 µg/ml en células de macrófagos de ratones, en los cuales no se encontró actividad citotóxica. Además, se evaluó la actividad leishmanicida por medio de estudios experimentales in vitro a concentraciones de 100, 75, 50, 25, 20 y 10 µg/ml frente a tres cepas de parásitos: Leishmania infantum, L. amazonensis y L. braziliensis, en los cuales se observó una actividad de 58 y 56% de lisis de parásitos con el extracto de 100µg/ml frente a las cepas de L. braziliensis y L. infantum, respectivamente y 51% para L. amazonensis. Estos resultados son prometedores, y aportan una base para el desarrollo y búsqueda de nuevos tratamientos para la leishmaniasis, sin embargo, son necesarios estudios posteriores en cuanto a aislamiento e identificación del compuesto y evaluación en modelos animales in vivo.