Maximum Common Substructure Searching in Combinatorial Make-on-Demand Compound Spaces

Open-Source Approach to GPU-Accelerated Substructure Search

Chemical substructure search is a critical task in medicinal chemistry and small-molecule drug discovery, enabling the retrieval of molecules from databases based on specific chemical features. While systems exist for this purpose, the challenge of efficient and swift searching persists, particularly as data storage migrates to the cloud, introducing new complexities. This study provides a comprehensive analysis of chemical substructure searches, showcasing the benefits of graphics processing unit-accelerated fingerprint screening. The research highlights strategies for optimizing performance, making significant advancements in substructure searching, a pivotal aspect of drug discovery and molecular research. The accessible and scalable nature of the proposed approach makes it a valuable resource for scientists aiming to enhance their substructure search capabilities.

In silico screening combined with bioactivity evaluation to identify AMI-1 as a novel anticancer compound by targeting AXL

Recently, some studies have proven that AXL plays a crucial role in the drug resistance of tumors. At present, no AXL inhibitors on the market and it is essential to discover novel compounds targeting AXL to overcome resistance. In this work, based on the anchor structure, 21,313 compounds were obtained by substructure search from more than 400,000 compounds. Then, the Qvina and Ledock were selected for virtual screening to obtain 17 compounds. Next, four compounds (ARRY614, AMI-1, NG25, and Butein) were selected for bioactivity evaluation after hydrogen bond and cluster analysis. Further activity evaluation suggested that the compound AMI-1 is a novel AXL inhibitor with an IC50 value of 1.13 uM. In addition, molecular dynamics simulation demonstrated that compound AMI-1 contained lower binding energy and more key residues than the other three compounds, showing the best inhibitory activity against AXL. Finally, further MM/PBSA prediction showed that AMI-1 is more sensitive to mutant protein 3IKA than wildtype protein 1M17, which means that the AMI-1 may be helpful to overcome the resistance of EGFRT790M mutations. In conclusion, this work successfully discovered a novel compound with moderate inhibitory activity against AXL by a drug discovery workflow, which also could be applied to discover active compounds for other targets quickly.Communicated by Ramaswamy H. Sarma.

Perspectives on current approaches to virtual screening in drug discovery

INTRODUCTION

For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods.

AREAS COVERED

The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS.

EXPERT OPINION

The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.

Navigating the complexity of p53-DNA binding: implications for cancer therapy

Abstract

The tumor suppressor protein p53, a transcription factor playing a key role in cancer prevention, interacts with DNA as its primary means of determining cell fate in the event of DNA damage. When it becomes mutated, it opens damaged cells to the possibility of reproducing unchecked, which can lead to formation of cancerous tumors. Despite its critical role, therapies at the molecular level to restore p53 native function remain elusive, due to its complex nature. Nevertheless, considerable information has been amassed, and new means of investigating the problem have become available.

Objectives

We consider structural, biophysical, and bioinformatic insights and their implications for the role of direct and indirect readout and how they contribute to binding site recognition, particularly those of low consensus. We then pivot to consider advances in computational approaches to drug discovery.

Materials and methods

We have conducted a review of recent literature pertinent to the p53 protein.

Results

Considerable literature corroborates the idea that p53 is a complex allosteric protein that discriminates its binding sites not only via consensus sequence through direct H-bond contacts, but also a complex combination of factors involving the flexibility of the binding site. New computational methods have emerged capable of capturing such information, which can then be utilized as input to machine learning algorithms towards the goal of more intelligent and efficient de novo allosteric drug design.

Conclusions

Recent improvements in machine learning coupled with graph theory and sector analysis hold promise for advances to more intelligently design allosteric effectors that may be able to restore native p53-DNA binding activity to mutant proteins.

Clinical relevance

The ideas brought to light by this review constitute a significant advance that can be applied to ongoing biophysical studies of drugs for p53, paving the way for the continued development of new methodologies for allosteric drugs. Our discoveries hold promise to provide molecular therapeutics which restore p53 native activity, thereby offering new insights for cancer therapies.

Graphical Abstract

Structural representation of the p53 DBD (PDBID 1TUP). DNA consensus sequence is shown in gray, and the protein is shown in blue. Red beads indicate hotspot residue mutations, green beads represent DNA interacting residues, and yellow beads represent both.

Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2

We herewith applied a priori a generic hit identification method (POEM) for difficult targets of known three-dimensional structure, relying on the simple knowledge of physicochemical and topological properties of a user-selected cavity. Searching for local similarity to a set of fragment-bound protein microenvironments of known structure, a point cloud registration algorithm is first applied to align known subpockets to the target cavity. The resulting alignment then permits us to directly pose the corresponding seed fragments in a target cavity space not typically amenable to classical docking approaches. Last, linking potentially connectable atoms by a deep generative linker enables full ligand enumeration. When applied to the WD40 repeat (WDR) central cavity of leucine-rich repeat kinase 2 (LRRK2), an unprecedented binding site, POEM was able to quickly propose 94 potential hits, five of which were subsequently confirmed to bind in vitro to LRRK2-WDR.

The IMS Library: from IN-Stock to Virtual

A chemical library is a key element in the early stages of pharmaceutical research. Its design encompasses various factors, such as diversity, size, ease of synthesis, aimed at increasing the likelihood of success in drug discovery. This article explores the collaborative efforts of computational and synthetic chemists in tailoring chemical libraries for cost-effective and resource-efficient use, particularly in the context of academic research projects. It proposes chemoinformatics methodologies that address two pivotal questions: first, crafting a diverse panel of under 1000 compounds from an existing pool through synthetic efforts, leveraging the expertise of organic chemists; and second, expanding pharmacophoric diversity within this panel by creating a highly accessible virtual chemical library. Chemoinformatics tools were developed to analyse initial panel of about 10,000 compounds into two tailored libraries: eIMS and vIMS. The eIMS Library comprises 578 diverse in-stock compounds ready for screening. Its virtual counterpart, vIMS, features novel compounds guided by chemists, ensuring synthetic accessibility. vIMS offers a broader array of binding motifs and improved drug-like characteristics achieved through the addition of diverse functional groups to eIMS scaffolds followed by filtering of reactive or unusual structures. The uniqueness of vIMS is emphasized through a comparison with commercial suppliers' virtual chemical space.

Carbenoid-involved reactions integrated with scaffold-based screening generates a Nav1.7 inhibitor

The discovery of selective Nav1.7 inhibitors is a promising approach for developing anti-nociceptive drugs. In this study, we present a novel oxindole-based readily accessible library (OREAL), which is characterized by readily accessibility, unique chemical space, ideal drug-like properties, and structural diversity. We used a scaffold-based approach to screen the OREAL and discovered compound C4 as a potent Nav1.7 inhibitor. The bioactivity characterization of C4 reveals that it is a selective Nav1.7 inhibitor and effectively reverses Paclitaxel-induced neuropathic pain (PINP) in rodent models. Preliminary toxicology study shows C4 is negative to hERG. The consistent results of molecular docking and molecular simulations further support the reasonability of the in-silico screening and show the insight of the binding mode of C4. Our discovery of C4 paves the way for pushing the Nav1.7-based anti-nociceptive drugs forward to the clinic.

Chemoinformatic approaches for navigating large chemical spaces

INTRODUCTION

Large chemical spaces (CSs) include traditional large compound collections, combinatorial libraries covering billions to trillions of molecules, DNA-encoded chemical libraries comprising complete combinatorial CSs in a single mixture, and virtual CSs explored by generative models. The diverse nature of these types of CSs require different chemoinformatic approaches for navigation.

AREAS COVERED

An overview of different types of large CSs is provided. Molecular representations and similarity metrics suitable for large CS exploration are discussed. A summary of navigation of CSs in generative models is provided. Methods for characterizing and comparing CSs are discussed.

EXPERT OPINION

The size of large CSs might restrict navigation to specialized algorithms and limit it to considering neighborhoods of structurally similar molecules. Efficient navigation of large CSs not only requires methods that scale with size but also requires smart approaches that focus on better but not necessarily larger molecule selections. Deep generative models aim to provide such approaches by implicitly learning features relevant for targeted biological properties. It is unclear whether these models can fulfill this ideal as validation is difficult as long as the covered CSs remain mainly virtual without experimental verification.

On the hunt for metalloenzyme inhibitors: Investigating the presence of metal-coordinating compounds in screening libraries and chemical spaces

Metalloenzymes play vital roles in various biological processes, requiring the search for inhibitors to develop treatment options for diverse diseases. While compound library screening is a conventional approach, the exploration of virtual chemical spaces housing trillions of compounds has emerged as an alternative strategy. In this study, we investigated the suitability of selected screening libraries and chemical spaces for discovering inhibitors of metalloenzymes featuring common ions (Mg2+, Mn2+, and Zn2+). First, metal-coordinating groups from ligands interacting with ions in the Protein Data Bank were extracted. Subsequently, the prevalence of these groups in two focused screening libraries (Life Chemicals' chelator library, comprising 6,428 compounds, and Otava's chelator fragment library, with 1,784 fragments) as well as two chemical spaces (GalaXi and REAL space, containing billions of virtual products) was investigated. In total, 1,223 metal-coordinating groups were identified, with about a quarter of these groups found within the examined libraries and spaces. Our results indicate that these can serve as valuable starting points for drug discovery targeting metalloenzymes. In addition, this study suggests ways to improve libraries and spaces for better success in finding potential inhibitors for metalloenzymes.

Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces

Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.

Enhanced Calculation of Property Distributions in Chemical Fragment Spaces

Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them. We extend the original algorithm by the capabilities to compute distributions for the TPSA, the number of rotatable bonds, and the occurrence of user-defined molecular structures in the form of SMARTS patterns. Furthermore, SpaceProp2 produces example molecules for every property bin, enabling a detailed interpretation of the distributions. We demonstrate SpaceProp2 on six established make-on-demand chemical fragment spaces as well as BICLAIM, the in-house fragment space of Boehringer Ingelheim. The possibility to search multiple SMARTS patterns simultaneously as well as the produced example molecules offers previously impossible insights into the composition of these vast combinatorial molecule collections, making it an ideal tool for the analysis and design of chemical fragment spaces.

SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces

The growing size of make-on-demand chemical libraries is posing new challenges to cheminformatics. These ultra-large chemical libraries became too large for exhaustive enumeration. Using a combinatorial approach instead, the resource requirement scales approximately with the number of synthons instead of the number of molecules. This gives access to billions or trillions of compounds as so-called chemical spaces with moderate hardware and in a reasonable time frame. While extremely performant ligand-based 2D methods exist in this context, 3D methods still largely rely on exhaustive enumeration and therefore fail to apply. Here, we present SpaceGrow: a novel shape-based 3D approach for ligand-based virtual screening of billions of compounds within hours on a single CPU. Compared to a conventional superposition tool, SpaceGrow shows comparable pose reproduction capacity based on RMSD and superior ranking performance while being orders of magnitude faster. Result assessment of two differently sized subsets of the eXplore space reveals a higher probability of finding superior results in larger spaces highlighting the potential of searching in ultra-large spaces. Furthermore, the application of SpaceGrow in a drug discovery workflow was investigated in four examples involving G protein-coupled receptors (GPCRs) with the aim to identify compounds with similar binding capabilities and molecular novelty.

Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces

Virtual screening of large-scale chemical libraries has become increasingly useful for identifying high-quality candidates for drug discovery. While it is possible to exhaustively screen chemical spaces that number on the order of billions, indirect combinatorial approaches are needed to efficiently navigate larger, synthon-based virtual spaces. We describe Shape-Aware Synthon Search (SASS), a synthon-based virtual screening method that carries out shape similarity searches in the synthon space instead of the enumerated product space. SASS can replicate results from exhaustive searches in ultralarge, combinatorial spaces with high recall on a variety of query molecules while only scoring a small subspace of possible enumerated products, thereby significantly accelerating large-scale, shape-based virtual screening.

"You've got the Body I've got the Brains" - Could the current AI-based tools replace the human ingenuity for designing new drug candidates?

The emergence of artificial intelligence (AI) tools has transformed the landscape of drug discovery, providing unprecedented speed, efficiency, and cost-effectiveness in the search for new therapeutics. From target identification to drug formulation and delivery, AI-driven algorithms have revolutionized various aspects of medicinal chemistry, significantly accelerating the drug design process. Despite the transformative power of AI, this perspective article emphasizes the limitations of AI tools in drug discovery, requiring inventive skills of medicinal chemists. However, the article highlighted that there is a need for a harmonious integration of AI-based tools and human expertise in drug discovery. Such a synergistic approach promises to lead to groundbreaking therapies that address unmet medical needs and benefit humankind. As the world evolves technologically, the question remains: When will AI tools effectively design and develop drugs? The answer may lie in the seamless collaboration between AI and human researchers, unlocking transformative therapies that combat diseases effectively.

School of cheminformatics in Latin America

We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24-25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for the meeting. As part of the meeting, advances in enumeration and visualization of chemical space, applications in natural product-based drug discovery, drug discovery for neglected diseases, toxicity prediction, and general guidelines for data analysis were discussed. Experts from ChEMBL presented a workshop on how to use the resources of this major compounds database used in cheminformatics. The school also included a round table with editors of cheminformatics journals. The full program of the meeting and the recordings of the sessions are publicly available at https://www.youtube.com/@SchoolChemInfLA/featured .

Fast Substructure Search in Combinatorial Library Spaces

We present an efficient algorithm for substructure search in combinatorial libraries defined by synthons, i.e., substructures with connection points. Our method improves on existing approaches by introducing powerful heuristics and fast fingerprint screening to quickly eliminate branches of nonmatching combinations of synthons. With this, we achieve typical response times of a few seconds on a standard desktop computer for searches in large combinatorial libraries like the Enamine REAL Space. We published the Java source as part of the OpenChemLib under the BSD license, and we implemented tools to enable substructure search in custom combinatorial libraries.

Relevance of the Trillion-Sized Chemical Space "eXplore" as a Source for Drug Discovery

Within the past two decades, virtual combinatorial compound collections, so-called chemical spaces, became an important molecule source for pharmaceutical research all over the world. The emergence of compound vendor chemical spaces with rapidly growing numbers of molecules raises questions about their application suitability and the quality of the content. Here, we examine the composition of the recently published and, so far, biggest chemical space, "eXplore", which comprises approximately 2.8 trillion virtual product molecules. The utility of eXplore to retrieve interesting chemistry around approved drugs and common Bemis Murcko scaffolds has been assessed with several methods (FTrees, SpaceLight, SpaceMACS). Further, the overlap between several vendor chemical spaces and a physicochemical property distribution analysis has been performed. Despite the straightforward chemical reactions underlying its setup, eXplore is demonstrated to provide relevant and, most importantly, easily accessible molecules for drug discovery campaigns.

Navigating large chemical spaces in early-phase drug discovery

The size of actionable chemical spaces is surging, owing to a variety of novel techniques, both computational and experimental. As a consequence, novel molecular matter is now at our fingertips that cannot and should not be neglected in early-phase drug discovery. Huge, combinatorial, make-on-demand chemical spaces with high probability of synthetic success rise exponentially in content, generative machine learning models go hand in hand with synthesis prediction, and DNA-encoded libraries offer new ways of hit structure discovery. These technologies enable to search for new chemical matter in a much broader and deeper manner with less effort and fewer financial resources. These transformational developments require new cheminformatics approaches to make huge chemical spaces searchable and analyzable with low resources, and with as little energy consumption as possible. Substantial progress has been made in the past years with respect to computation as well as organic synthesis. First examples of bioactive compounds resulting from the successful use of these novel technologies demonstrate their power to contribute to tomorrow's drug discovery programs. This article gives a compact overview of the state-of-the-art.

Automated detection of toxicophores and prediction of mutagenicity using PMCSFG algorithm

Maximum common substructures (MCS) have received a lot of attention in the chemoinformatics community. They are typically used as a similarity measure between molecules, showing high predictive performance when used in classification tasks, while being easily explainable substructures. In the present work, we applied the Pairwise Maximum Common Subgraph Feature Generation (PMCSFG) algorithm to automatically detect toxicophores (structural alerts) and to compute fingerprints based on MCS. We present a comparison between our MCS-based fingerprints and 12 well-known chemical fingerprints when used as features in machine learning models. We provide an experimental evaluation and discuss the usefulness of the different methods on mutagenicity data. The features generated by the MCS method have a state-of-the-art performance when predicting mutagenicity, while they are more interpretable than the traditional chemical fingerprints.

ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery

Purchasable chemical space has grown rapidly into the tens of billions of molecules, providing unprecedented opportunities for ligand discovery but straining the tools that might exploit these molecules at scale. We have therefore developed ZINC-22, a database of commercially accessible small molecules derived from multi-billion-scale make-on-demand libraries. The new database and tools enable analog searching in this vast new space via a facile GUI, CartBlanche, drawing on similarity methods that scale sublinearly in the number of molecules. The new library also uses data organization methods, enabling rapid lookup of molecules and their physical properties, including conformations, partial atomic charges, c Log P values, and solvation energies, all crucial for molecule docking, which had become slow with older database organizations in previous versions of ZINC. As the libraries have continued to grow, we have been interested in finding whether molecular diversity has suffered, for instance, because certain scaffolds have come to dominate via easy analoging. This has not occurred thus far, and chemical diversity continues to grow with database size, with a log increase in Bemis-Murcko scaffolds for every two-log unit increase in database size. Most new scaffolds come from compounds with the highest heavy atom count. Finally, we consider the implications for databases like ZINC as the libraries grow toward and beyond the trillion-molecule range. ZINC is freely available to everyone and may be accessed at cartblanche22.docking.org, via Globus, and in the Amazon AWS and Oracle OCI clouds.

Overlap of On-demand Ultra-large Combinatorial Spaces with On-the-shelf Drug-like Libraries

On-demand combinatorial spaces are shifting paradigms in early drug discovery, by considerably increasing the searchable chemical space to several billions of compounds while securing their synthetic accessibility. We here systematically compared the on-the-shelf available drug-like chemical space (9 million compounds) to three on-demand ultra-large (ODUL) combinatorial fragment spaces (REAL, CHEMriya, GalaXi) covering 32 billion of readily accessible molecules. Surprisingly, only one space (REAL) intersects almost entirely the currently available drug-like space, suggesting that it is the only ODUL widely suitable for in-stock hit expansion. Of course, expanding a preliminary ODUL hit in the same chemical space is the best possible strategy to rapidly generate structure-activity relationships. All three spaces remain well suited to early hit finding initiatives since they all provide numerous unique scaffolds that are not described by on-the shelf collections.

Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores

Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine's REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.

Magnet for the Needle in Haystack: "Crystal Structure First" Fragment Hits Unlock Active Chemical Matter Using Targeted Exploration of Vast Chemical Spaces

Fragment-based drug discovery (FBDD) has successfully led to approved therapeutics for challenging and "undruggable" targets. In the context of FBDD, we introduce a novel, multidisciplinary method to identify active molecules from purchasable chemical space. Starting from four small-molecule fragment complexes of protein kinase A (PKA), a template-based docking screen using Enamine's multibillion REAL Space was performed. A total of 93 molecules out of 106 selected compounds were successfully synthesized. Forty compounds were active in at least one validation assay with the most active follow-up having a 13,500-fold gain in affinity. Crystal structures for six of the most promising binders were rapidly obtained, verifying the binding mode. The overall success rate for this novel fragment-to-hit approach was 40%, accomplished in only 9 weeks. The results challenge the established fragment prescreening paradigm since the standard industrial filters for fragment hit identification in a thermal shift assay would have missed the initial fragments.

Chemical Multiverse: An Expanded View of Chemical Space

Technological advances and practical applications of the chemical space concept in drug discovery, natural product research, and other research areas have attracted the scientific community's attention. The large- and ultra-large chemical spaces are associated with the significant increase in the number of compounds that can potentially be made and exist and the increasing number of experimental and calculated descriptors, that are emerging that encode the molecular structure and/or property aspects of the molecules. Due to the importance and continued evolution of compound libraries, herein, we discuss definitions proposed in the literature for chemical space and emphasize the convenience, discussed in the literature to use complementary descriptors to obtain a comprehensive view of the chemical space of compound data sets. In this regard, we introduce the term chemical multiverse to refer to the comprehensive analysis of compound data sets through several chemical spaces, each defined by a different set of chemical representations. The chemical multiverse is contrasted with a related idea: consensus chemical space.

Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces

The distributions of physicochemical property values, like the octanol-water partition coefficient, are routinely calculated to describe and compare virtual chemical libraries. Traditionally, these distributions are derived by processing each member of a library individually and summarizing all values in a distribution. This process becomes impractical when operating on chemical spaces which surpass billions of compounds in size. In this work, we present a novel algorithmic method called SpaceProp for the property distribution calculation of large nonenumerable combinatorial fragment spaces. The novel method follows a combinatorial approach and is able to calculate physicochemical property distributions of prominent spaces like Enamine's REAL Space, WuXi's GalaXi Space, and OTAVA's CHEMriya Space for the first time. Furthermore, we present a first approach of optimizing property distributions directly in combinatorial fragment spaces.

Exploration of Ultralarge Compound Collections for Drug Discovery

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring chemical space more widely and efficiently. Chemical space is monumentally large, but recent advances in computer software and hardware have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures. This review specifically concerns collections of many millions or even billions of enumerated chemical structures as well as even larger chemical spaces that are not fully enumerated. We present examples of chemical libraries and spaces and the means used to construct them, and we discuss new technologies for searching huge libraries and for searching combinatorially in chemical space. We also cover space navigation techniques and consider new approaches to de novo drug design and the impact of the "autonomous laboratory" on synthesis of designed compounds. Finally, we summarize some other challenges and opportunities for the future.

ProBiS-Dock: A Hybrid Multitemplate Homology Flexible Docking Algorithm Enabled by Protein Binding Site Comparison

The protein data bank (PDB) is a rich source of protein ligand structures, but ligands are not explicitly used in current docking algorithms. We have developed ProBiS-Dock, a docking algorithm complementary to the ProBiS-Dock Database (J. Chem. Inf. Model. 2021, 61, 4097-4107) that treats small molecules and proteins as fully flexible entities and allows conformational changes in both after ligand binding. A new scoring function is described that consists of a binding site-specific scoring function (ProBiS-Score) and a general statistical scoring function. ProBiS-Dock enables rapid docking of small molecules to proteins and has been successfully validated in silico against standard benchmarks. It enables rapid search for new active ligands by leveraging existing knowledge in the PDB. The potential of the software for drug development has been confirmed in vitro by the discovery of new inhibitors of human indoleamine 2,3-dioxygenase 1, an enzyme that is an attractive target for cancer therapy and catalyzes the first rate-determining step of l-tryptophan metabolism via the kynurenine pathway. The software is freely available to academic users at http://insilab.org/probisdock.