Fragment-based Drug Design Research Articles

Accessing a Medicinal-Chemistry-Relevant Chemical Space with sp2–sp3 Hybrid Heterocyclic Fragments

AbstractTarget-first drug discovery relies heavily on protein structure information, which severely limits its application. In recent years, fragment-based drug Design (FBDD) has been identified as an alternative solution, where screening of smaller molecules for lower affinity allowed the use of focused libraries with a higher hit rate. It is shown that coupling an sp2-rich heteroaromatic group with a monofunctional sp3-rich core gives fragments (186 examples) with advantageous physical-chemical properties, covering a chemical space often neglected in traditional libraries.

CLigOpt: controllable ligand design through target-specific optimization.

A key challenge in deep generative models for molecular design is to navigate random sampling of the vast molecular space, and produce promising molecules that strike a balance across multiple chemical criteria. Fragment-based drug design (FBDD), using fragments as starting points, is an effective way to constrain chemical space and improve generation of biologically active molecules. Furthermore, optimization approaches are often implemented with generative models to search through chemical space, and identify promising samples which satisfy specific properties. Controllable FBDD has promising potential in efficient target-specific ligand design. We propose a controllable FBDD model, CLigOpt, which can generate molecules with desired properties from a given fragment pair. CLigOpt is a variational autoencoder-based model which utilizes co-embeddings of node and edge features to fully mine information from molecular graphs, as well as a multi-objective Controllable Generation Module to generate molecules under property controls. CLigOpt achieves consistently strong performance in generating structurally and chemically valid molecules, as evaluated across six metrics. Applicability is illustrated through ligand candidates for hDHFR and it is shown that the proportion of feasible active molecules from the generated set is increased by 10%. Molecular docking and synthesizability prediction tasks are conducted to prioritize generated molecules to derive potential lead compounds. The source code is available via https://github.com/yutongLi1997/CLigOpt-Controllable-Ligand-Design-through-Target-Specific-Optimisation.

Probing Protein-Ligand Methyl-π Interaction Geometries through Chemical Shift Measurements of Selectively Labeled Methyl Groups.

Fragment-based drug design is heavily dependent on the optimization of initial low-affinity binders. Herein we introduce an approach that uses selective labeling of methyl groups in leucine and isoleucine side chains to directly probe methyl-π contacts, one of the most prominent forms of interaction between proteins and small molecules. Using simple NMR chemical shift perturbation experiments with selected BRD4-BD1 binders, we find good agreement with a commonly used model of the ring-current effect as well as the overall interaction geometries extracted from the Protein Data Bank. By combining both interaction geometries and chemical shift calculations as fit quality criteria, we can position dummy aromatic rings into an AlphaFold model of the protein of interest. The proposed method can therefore provide medicinal chemists with important information about binding geometries of small molecules in fast and iterative matter, even in the absence of high-resolution experimental structures.

DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras.

PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure-activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence-generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs.

High-confidence placement of low-occupancy fragments into electron density using the anomalous signal of sulfur and halogen atoms.

Fragment-based drug design using X-ray crystallography is a powerful technique to enable the development of new lead compounds, or probe molecules, against biological targets. This study addresses the need to determine fragment binding orientations for low-occupancy fragments with incomplete electron density, an essential step before further development of the molecule. Halogen atoms play multiple roles in drug discovery due to their unique combination of electronegativity, steric effects and hydrophobic properties. Fragments incorporating halogen atoms serve as promising starting points in hit-to-lead development as they often establish halogen bonds with target proteins, potentially enhancing binding affinity and selectivity, as well as counteracting drug resistance. Here, the aim was to unambiguously identify the binding orientations of fragment hits for SARS-CoV-2 nonstructural protein 1 (nsp1) which contain a combination of sulfur and/or chlorine, bromine and iodine substituents. The binding orientations of carefully selected nsp1 analogue hits were focused on by employing their anomalous scattering combined with Pan-Dataset Density Analysis (PanDDA). Anomalous difference Fourier maps derived from the diffraction data collected at both standard and long-wavelength X-rays were compared. The discrepancies observed in the maps of iodine-containing fragments collected at different energies were attributed to site-specific radiation-damage stemming from the strong X-ray absorption of I atoms, which is likely to cause cleavage of the C-I bond. A reliable and effective data-collection strategy to unambiguously determine the binding orientations of low-occupancy fragments containing sulfur and/or halogen atoms while mitigating radiation damage is presented.

Explore new quinoxaline pharmacophore tethered sulfonamide fragments as in vitro α-glucosidase, α-amylase, and acetylcholinesterase inhibitors with ADMET and molecular modeling simulation.

A new series of quinoxaline-sulfonamide derivatives 3-12 were synthesized using fragment-based drug design by reaction of quinoxaline sulfonyl chloride (QSC) with different amines and hydrazines. The quinoxaline-sulfonamide derivatives were evaluated for antidiabetic and anti-Alzheimer's potential against α-glucosidase, α-amylase, and acetylcholinesterase enzymes. These derivatives showed good to moderate potency against α-amylase and α-glucosidase with inhibitory percentages between 24.34 ± 0.01%-63.09 ± 0.02% and 28.95 ± 0.04%-75.36 ± 0.01%, respectively. Surprisingly, bis-sulfonamide quinoxaline derivative 4 revealed the most potent activity with inhibitory percentages of 75.36 ± 0.01% and 63.09 ± 0.02% against α-glucosidase and α-amylase compared to acarbose (IP = 57.79 ± 0.01% and 67.33 ± 0.01%), respectively. Moreover, the quinoxaline derivative 3 exhibited potency as α-glucosidase and α-amylase inhibitory with a minute decline from compound 4 and acarbose with inhibitory percentages of 44.93 ± 0.01% and 38.95 ± 0.01%. Additionally, in vitro acetylcholinesterase inhibitory activity for designed derivatives exhibited weak to moderate activity. Still, sulfonamide-quinoxaline derivative 3 emerged as the most active member with inhibitory percentage of 41.92 ± 0.02% compared with donepezil (IP = 67.27 ± 0.60%). The DFT calculations, docking simulation, target prediction, and ADMET analysis were performed and discussed in detail.

Quantum-assisted fragment-based automated structure generator (QFASG) for small molecule design: an in vitro study.

Introduction: The significance of automated drug design using virtual generative models has steadily grown in recent years. While deep learning-driven solutions have received growing attention, only a few modern AI-assisted generative chemistry platforms have demonstrated the ability to produce valuable structures. At the same time, virtual fragment-based drug design, which was previously less popular due to the high computational costs, has become more attractive with the development of new chemoinformatic techniques and powerful computing technologies. Methods: We developed Quantum-assisted Fragment-based Automated Structure Generator (QFASG), a fully automated algorithm designed to construct ligands for a target protein using a library of molecular fragments. QFASG was applied to generating new structures of CAMKK2 and ATM inhibitors. Results: New low-micromolar inhibitors of CAMKK2 and ATM were designed using the algorithm. Discussion: These findings highlight the algorithm's potential in designing primary hits for further optimization and showcase the capabilities of QFASG as an effective tool in this field.

Abstract 7273: Small molecule activation of PP2A in the treatment of melanoma

Abstract Protein phosphatase 2A (PP2A) is a heterotrimeric holoenzyme functioning as a serine/threonine phosphatase, where it accounts for about 50-70% of all serine/threonine phosphorylation in the cell. Yet, PP2A is commonly inactivated in cancer, likely due to its role as a tumor suppressor. Due to the important role PP2A plays in many cellular processes, including those involved with tumor progression, there is a current need for novel therapeutics capable of targeting PP2A in cancer. Currently, none of the current PP2A modulators have made it into approved clinical use. As such, we have recently synthesized the PP2A activators, PPA24 and PPA27, using molecular docking and fragment-based drug design based on the structures of recently reported PP2A activators, iHAP1 and NSC49L. We were previously able to establish PPA24 as the lead compound in treating colorectal cancer (CRC) in vitro, compared to PPA27 and the parent compounds. Nevertheless, a single dose NCI-60 screen of both PPA24 and PPA27 indicated the cell selectivity present in both compounds and revealed the potential effect these compounds could have in other cancers. Further evaluation with an extended NCI-60 5-dose screen of PPA24 and PPA27 demonstrated a strong potential for both compounds in treating melanoma. Overall, the NCI-60 screen displayed PPA27 as most effective in melanoma across all cell lines screened, while PPA24 was found to be effective in both CRC and melanoma cell lines, agreeing with our previous findings. A cell viability screening of PPA24 and PPA27 in our laboratory across 5 melanoma cell lines (SK-MEL-2, SK-MEL-28, A375, Mel1241, and 451Lu) identified IC50 values in the range of 3.45-8.94 µM for PPA27 and 5.99-14.45 µM for PPA24. The parent compounds, iHAP1 and NSC49L, were also evaluated and NSC49L was found to be less effective in melanoma than previously found in CRC cells. Both PPA24 and PPA27 were found to be effective in cell lines with and without BRAFV600E mutation, though it appears the metastatic cell line, 451Lu, required an increased concentration for all compounds as compared to the IC50 values found in the other melanoma cell lines. Further evaluation revealed PPA24 and PPA27 as dose-dependently inducing apoptosis in cells with BRAFV600E (A375) and BRAF WT (SK-MEL-2). Together, these findings show the potential of PPA24 and PPA27 as therapeutic treatments for melanoma with or without BRAF mutations and are currently being further evaluated in several in vitro and in vivo melanoma models in our laboratory. Citation Format: Hannah Johnson, Amandeep Singh, Asif Raza, Satya Narayan, Arun K. Sharma. Small molecule activation of PP2A in the treatment of melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7273.

Fragment-based drug design of novel inhibitors targeting lipoprotein (a) kringle domain KIV-10-mediated cardiovascular disease.

Cardiovascular diseases (CVDs) are the leading cause of death globally, attributed to a complex etiology involving metabolic, genetic, and protein-related factors. Lipoprotein(a) (Lp(a)), identified as a genetic risk factor, exhibits elevated levels linked to an increased risk of cardiovascular diseases. The lipoprotein(a) kringle domains have recently been identified as a potential target for the treatment of CVDs, in this study we utilized a fragment-based drug design approach to design a novel, potent, and safe inhibitor for lipoprotein(a) kringle domain. With the use of fragment library (61,600 fragments) screening, combined with analyses such as MM/GBSA, molecular dynamics simulation (MD), and principal component analysis, we successfully identified molecules effective against the kringle domains of Lipoprotein(a). The hybridization process (Breed) of the best fragments generated a novel 249 hybrid molecules, among them 77 exhibiting superior binding affinity (≤ -7kcal/mol) compared to control AZ-02 (-6.9kcal/mol), Importantly, the top ten molecules displayed high similarity to the control AZ-02. Among the top ten molecules, BR1 exhibited the best docking energy (-11.85kcal/mol ), and higher stability within the protein LBS site, demonstrating the capability to counteract the pathophysiological effects of lipoprotein(a) [Lp(a)]. Additionally, principal component analysis (PCA) highlighted a similar trend of motion during the binding of BR1 and the control compound (AZ-02), limiting protein mobility and reducing conformational space. Moreover, ADMET analysis indicated favorable drug-like properties, with BR1 showing minimal violations of Lipinski's rules. Overall, the identified compounds hold promise as potential therapeutics, addressing a critical need in cardiovascular medicine. Further preclinical and clinical evaluations are needed to validate their efficacy and safety, potentially ushering in a new era of targeted therapies for CVDs.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Generating Potential RET-Specific Inhibitors Using a Novel LSTM Encoder-Decoder Model.

The receptor tyrosine kinase RET (rearranged during transfection) plays a vital role in various cell signaling pathways and is a critical factor in the development of the nervous system. Abnormal activation of the RET kinase can lead to several cancers, including thyroid cancer and non-small-cell lung cancer. However, most RET kinase inhibitors are multi-kinase inhibitors. Therefore, the development of an effective RET-specific inhibitor continues to present a significant challenge. To address this issue, we built a molecular generation model based on fragment-based drug design (FBDD) and a long short-term memory (LSTM) encoder-decoder structure to generate receptor-specific molecules with novel scaffolds. Remarkably, our model was trained with a molecular assembly accuracy of 98.4%. Leveraging the pre-trained model, we rapidly generated a RET-specific-candidate active-molecule library by transfer learning. Virtual screening based on our molecular generation model was performed, combined with molecular dynamics simulation and binding energy calculation, to discover specific RET inhibitors, and five novel molecules were selected. Further analyses indicated that two of these molecules have good binding affinities and synthesizability, exhibiting high selectivity. Overall, this investigation demonstrates the capacity of our model to generate novel receptor-specific molecules and provides a rapid method to discover potential drugs.

Identification of Potential Cytochrome p450c 17 Alpha Inhibitors for the Treatment of PCOS via Scaffold Hopping and Fragment-Based De-Novo Drug Design

Identification of Potential Cytochrome p450c 17 Alpha Inhibitors for the Treatment of PCOS via Scaffold Hopping and Fragment-Based De-Novo Drug Design

In silico construction of a focused fragment library facilitating exploration of chemical space.

Fragment-based drug design (FBDD) has emerged as a captivating subject in the realm of computer-aided drug design, enabling the generation of novel molecules through the rearrangement of ring systems within known compounds. The construction of focused fragment library plays a pivotal role in FBDD, necessitating the compilation of all potential bioactive ring systems capable of interacting with a specific target. In our study, we propose a workflow for the development of a focused fragment library and combinatorial compound library. The fragment library comprises seed fragments and collected fragments. The extraction of seed fragments is guided by receptor information, serving as a prerequisite for establishing a focused libraries. Conversely, collected fragments are obtained using the feature graph method, which offers a simplified representation of fragments and strikes a balance between diversity and similarity when categorizing different fragments. The utilization of feature graph facilitates the rational partitioning of chemical space at fragment level, enabling the exploration of desired chemical space and enhancing the efficiency of screening compound library. Analysis demonstrates that our workflow enables the enumeration of a greater number of entirely new potential compounds, thereby aiding in the rational design of drugs.

Computational fragment-based drug design of potential Glo-I inhibitors

In this study, a fragment-based drug design approach, particularly de novo drug design, was implemented utilising three different crystal structures in order to discover new privileged scaffolds against glyoxalase-I enzyme as anticancer agents. The fragments were evoluted to indicate potential inhibitors with high receptor affinities. The resulting compounds were served as a benchmark for choosing similar compounds from the ASINEX® database by applying different computational ligand-based drug design techniques. Afterwards, the selection of potential hits was further aided by various structure-based approaches. Then, 14 compounds were purchased, and tested in vitro against Glo-I enzyme. Of the tested 14 hits, the biological screening results showed humble activities where the percentage of Glo-I inhibition ranged from 0–18.70 %. Compound 19 and compound 28, whose percentage of inhibitions are 18.70 and 15.80%, respectively, can be considered as hits that need further optimisation in order to be converted into lead-like compounds.

Identification of novel phenylalanine derivatives bearing a hydroxamic acid moiety as potent quorum sensing inhibitors.

Phenylalanine derivatives are a well-known small moiety responsible for controlling the virulence factors of several bacteria. Herein, for the first time, we report novel structures of phenylalanine derivatives bearing a hydroxamic acid moiety which were designed, synthesized, and evaluated for use as quorum sensing inhibitors. Biological results reveal that six compounds showed good quorum sensing inhibitors properties with an IC50 ranging from 7.12 ± 2.11 μM-92.34 ± 2.09 μM (4NPO, a reference compound, IC50 = 29.13 ± 0.88 μM). In addition, three out of the six compounds (4a, 4c, 4h) showed strong anti-biofilm formation and CviR inhibitory activity when compared to that of 4NPO. These biological data were also confirmed by computational studies. In this series of compounds, 4h is the most promising compound for future drug development targeting quorum sensing. Our results concluded that the fragment-based drug design is a good approach for the discovery of novel quorum-sensing inhibitors in the future.

Screening of efficient salicylaldoxime reactivators for DFP and paraoxon-inhibited acetylcholinesterase.

Previously we reported two salicylaldoxime conjugates (L7R3 and L7R5) showing equal or even higher reactivating efficiency for both organophosphorus nerve agent and pesticide inhibited acetylcholinesterase in comparison to obidoxime and HI-6. In this study, L7R3 and L7R5 were selected as lead compounds and refined by employing a fragment-based drug design strategy, and a total of 32 novel salicylaldoxime conjugates were constructed and screened for DFP and paraoxon inhibited acetylcholinesterase. The findings demonstrate that the conjugate L73R3, which contains a 4-nitrophenyl group, exhibited a higher reactivation efficacy against paraoxon-inhibited acetylcholinesterase compared to obidoxime and HI-6. It was confirmed that the combination of a 4-pyridinyl or 4-nitrophenyl peripheral site ligand, a piperazine linker and a methyl or chloro-substituted salicylaldoxime could construct efficient nonquaternary oxime reactivators. The results hold promise for developing a new generation of highly effective antidotes for organophosphate poisoning.

Decision tree-based identification of important molecular fragments for protein-ligand binding.

Fragment-based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein-ligand binding affinity. Specifically, the three-dimensional (3D) structures of protein-ligand complexes are encoded using extended-connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few-shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments.

Computational Design, Synthesis, and Bioevaluation of 2-(Pyrimidin-4-yl)oxazole-4-carboxamide Derivatives: Dual Inhibition of EGFRWT and EGFRT790M with ADMET Profiling

The ongoing research in cancer treatment underscores the significance of dual epidermal growth factor receptor (EGFR) kinase inhibitors targeting both mutant and wild-type variants. In this study, employing in silico fragment-based drug design (FBDD) and computational analysis, we have successfully developed a novel chemical series of 2-(pyrimidin-4-yl)oxazole-4-carboxamide (16a-j) derivatives designed as dual EGFR kinase inhibitors. A comparative in vitro anticancer profile of the newly synthesized compounds (16a-j) was tested against a panel of five human cancer cell lines like prostate cancer (PC3 & DU-145), lung cancer (A549), human liver cancer (HEPG2), and breast cancer (MDA-MB-468) by employing MTT method. In this experiment a well-known anticancer agent, Etoposide was used as positive control. Most of the derivatives demonstrated significant cytotoxicity, ranging from excellent to moderate levels. The IC50 values for the synthesized compounds observed between 0.10 ± 0.052 to 9.83 ± 5.96 µM, while the positive control exhibited a range of 1.97 ± 0.45 µM to 3.08 ± 0.135 µM. These results indicate that the synthesized compounds demonstrate higher cytotoxic potency in comparison to the reference compound. Furthermore, all these compounds underwent screening against normal Vero cell lines to assess their cytotoxicity. In each case, the observed cytotoxicity values (IC50) were higher than 22 µM, affirming the compounds selectivity for cancer cell lines. Among the compounds investigated, three compounds (16a, 16e, and 16i) exhibited notable cytotoxicity, while two compounds (16g and 16h) demonstrated exceptional cytotoxicity. The selectivity index of the tested compounds indicates a pronounced preference for targeting cancer cell lines over normal cells. Furthermore, all the compounds 16a-j underwent assessment for their EGFR kinase inhibitory activity against both EGFRWT and mutated EGFRT790M. The results unveiled the potential eligibility of this new series of compounds as effective EGFR inhibitors. Moreover, compound 16h underwent additional testing for cell cycle analysis, revealing its capability to arrest the cell cycle in the G2/M phase and induce apoptosis at the IC50 concentration.

Design, synthesis, and characterization of 3-(2-(pyrimidin-5-yl)thiazol-4-yl)-1,2,4-oxadiazole derivatives: Anticancer and molecular docking investigations

Herein, we present a new library of 3-(2-(pyrimidin-5-yl)thiazol-4-yl)-1,2,4-oxadiazole (14a–j) as an anticancer agent designed based on in silico fragment-based drug design (FBDD). The derivatives were synthesized and characterized by 1HNMR,13CNMR, HRMS, and FT-IR. The library was named “pyrimidine-thiazole-1,2,4-oxadiazoles.” In addition, the MTT assay was used to test the compounds (14a–j) for their potential anticancer activity in a panel of four human cancer cell lines, such as PC3 & DU-145 (prostate cancer), A549 (lung cancer), MCF-7 (breast cancer), and the normal Vero cell lines, using the widely used anticancer drug etoposide acting as a reference drug and positive control. The value obtained was presented as IC50 (µM). The IC50 values of the reference standard ranged from 1.97 ± 0.45 µM to 3.08 ± 0.135 µM, while the library of tested compounds showed IC50 values ranging from 0.02 ± 0.0031 µM to 10.3 ± 6.25 µM. Among the tested compounds, six of them showed promising anticancer activity: 14a, 14b, 14c, 14d, 14e and 14j. Especially, one compound, 14a, showed excellent anticancer activity in all the tested cell lines. All compounds showed selective cytotoxicity against cancer cells but not against normal Vero cells (IC50 = >19 µM), justifying the designing approach to develop a selective anticancer agent. These compounds were also subjected to docking experiments to investigate potential binding site interactions, and the results were consistent with the in vitro results.

Fragment databases from screened ligands for drug discovery (FDSL-DD)

Fragment-based drug design (FBDD) is one major drug discovery method employed in computer-aided drug discovery. Due to its inherent limitations, this process experiences long processing times and limited success rates. Here we present a new Fragment Databases from Screened Ligands Drug Design method (FDSL-DD) that intelligently incorporates information about fragment characteristics into a fragment-based design approach to the drug development process. The initial step of the FDSL-DD is the creation of a fragment database from a library of docked, drug-like ligands for a specific target, which deviates from the traditional in silico FBDD strategy, incorporating structure-based design screening techniques to combine the advantages of both approaches. Three different protein targets have been tested in this study to demonstrate the potential of the created fragment library and FDSL-DD. Utilizing the FDSL-DD led to an increase in binding affinity for each protein target. The most substantial increase was exhibited by the ligand designed for TIPE2, with a 3.6 kcalmol-1 difference between the top ligand from the FDSL-DD and top ligand from the high throughput virtual screening (HTVS). Using drug-like ligands in the initial HTVS allows for a greater search of chemical space, with higher efficiency in fragments selection, less grid boxes, and potentially identifying more interactions.