Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure−activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.

Extended-Connectivity Fingerprints

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

/pdf/computational-methods-in-drug-discovery-3sww6kbvd5.pdf

Computational Methods in Drug Discovery

Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points for medicinal chemistry. The number of methods and softwares which use the ligand and target-based VS approaches is increasing at a rapid pace. What, however, are the real advantages and disadvantages of the VS technology and how applicable is it to drug discovery projects? This review provides a comprehensive appraisal of several VS approaches currently available. In the first part of this work, an overview of the recent progress and advances in both ligand-based VS (LBVS) and structure-based VS (SBVS) strategies highlighting current problems and limitations will be provided. Special emphasis will be given to in silico chemogenomics approaches which utilize annotated ligand-target as well as protein-ligand interaction databases and which could predict or reveal promiscuous binding and polypharmacology, the knowledge of which would help medicinal chemists to design more potent clinical candidates with fewer side effects. In the second part, recent case studies (all published in the last two years) will be discussed where the VS technology has been applied successfully. A critical analysis of these case studies provides a good platform in order to estimate the applicability of various VS strategies in the new lead identification and optimization.

Virtual screening strategies in drug discovery: a critical review.

The aim of virtual screening (VS) is to identify bioactive compounds through computational means, by employing knowledge about the protein target (structure-based VS) or known bioactive ligands (ligand-based VS). In VS, a large number of molecules are ranked according to their likelihood to be bioactive compounds, with the aim to enrich the top fraction of the resulting list (which can be tested in bioassays afterward). At its core, VS attempts to improve the odds of identifying bioactive molecules by maximizing the true positive rate, that is, by ranking the truly active molecules as high as possible (and, correspondingly, the truly inactive ones as low as possible). In choosing the right approach, the researcher is faced with many questions: where does the optimal balance between efficiency and accuracy lie when evaluating a particular algorithm; do some methods perform better than others and in what particular situations; and what do retrospective results tell us about the prospective utility of a part...

Recognizing Pitfalls in Virtual Screening: A Critical Review

Refined nearest neighbor analysis was recently introduced for the analysis of virtual screening benchmark data sets. It constitutes a technique from the field of spatial statistics and provides a mathematical framework for the nonparametric analysis of mapped point patterns. Here, refined nearest neighbor analysis is used to design benchmark data sets for virtual screening based on PubChem bioactivity data. A workflow is devised that purges data sets of compounds active against pharmaceutically relevant targets from unselective hits. Topological optimization using experimental design strategies monitored by refined nearest neighbor analysis functions is applied to generate corresponding data sets of actives and decoys that are unbiased with regard to analogue bias and artificial enrichment. These data sets provide a tool for Maximum Unbiased Validation (MUV) of virtual screening methods. The data sets and a software package implementing the MUV design workflow are freely available at http://www.pharmchem....

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data.

4027 Background: DKN-01 is an anti-DKK1 mAb which has demonstrated anti-tumor activity in patients with advanced GEA with low tumor PD-L1 expression, a subset with very limited therapeutic options. DKN-01 has immunomodulatory activity, stimulates a pro-inflammatory tumor microenvironment, and upregulates PD-L1 levels. Here we present 2-year survival data for 1L advanced GEA patients who received combination treatment with DKN-01, the Fc-optimized anti-PD-1 tislelizumab, and CAPOX chemotherapy. The ORR was previously reported (68% in ITT and 79% in PD-L1-low populations). Methods: This Phase 2, multi-center, single arm Part A of the DisTinGuish (NCT04363801) study investigated DKN-01 + tislelizumab + CAPOX as 1L therapy in advanced HER2(-) GEA, regardless of DKK1 and PD-L1 expression levels. Tumor DKK1 and PD-L1 were assessed by central laboratories. The primary endpoint was ORR in a modified intent to treat population (>1 dose DKN-01); secondary endpoints included PFS and OS in the ITT population. Results: Twenty-five patients were enrolled from September 2020 to April 2021. As of 23 January 2023: Median age was 61 years (22, 80); 17 patients had GEJ adenocarcinoma; 8 had gastric cancer. Twenty-one patients had tumors with evaluable DKK1 expression. Twenty-two patients had tumors with evaluable PD-L1 expression; the majority (73%) were low expressors (vCPS <5%). Median (m) duration on treatment is 11.3 months (mo). Seven patients remain on study, with 4 on-treatment beyond 2 years. The mPFS is 11.3 mo in the overall ITT population and 10.7 mo in patients with low tumor PD-L1 expression. The mOS is 19.5 mo in the overall ITT population and 18.7 mo in the patients with low tumor PD-L1 expression. Treatment related adverse events (TRAEs) were mild with most G1/2. The most common AEs related to the study drug regimen were nausea (72%), diarrhea (64%) and fatigue (60%). Five patients experienced G3 DKN-01-TRAEs including decreased neutrophil count (1), diarrhea (1), vomiting (1), hypophosphatemia (2), and pulmonary embolism (1). Conclusions: At 2-years of follow-up, 1L treatment of advanced GEA patients with DKN-01 in combination with tislelizumab and CAPOX resulted in a prolongation of PFS and OS compared with the modern SOC regimen of nivolumab plus chemotherapy, both in the overall population (mPFS 11.3 vs 7.7 mo; mOS 19.5 vs 13.8 mo) and in the PD-L1 low subgroup (mPFS 10.7 vs 7.5 mo; mOS 18.7 vs 12.4 mo), alongside a manageable safety profile. The prolonged PFS and OS observed in the current study are notable, especially in our trial population dominated by patients with tumors of low PD-L1 expression, where the known benefit of anti-PD-1 therapy is limited. The ongoing Part C of this study is evaluating mFOLFOX6/CAPOX plus tislelizumab with or without DKN-01 in the same 1L GEA patient population. Further evaluation of biomarkers is also ongoing. Clinical trial information: NCT04363801 .

A phase 2 study (DisTinGuish) of DKN-01 in combination with tislelizumab + chemotherapy as first-line (1L) therapy in patients with advanced gastric or GEJ adenocarcinoma (GEA).

High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created “frequent hitter” models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed “off target” response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently p...

Understanding False Positives in Reporter Gene Assays: in Silico Chemogenomics Approaches To Prioritize Cell-Based HTS Data

The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naive Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naive Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.

Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results.

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A phase 2 study (DisTinGuish) of DKN-01 in combination with tislelizumab + chemotherapy as first-line (1L) therapy in patients with advanced gastric or GEJ adenocarcinoma (GEA).

Understanding False Positives in Reporter Gene Assays: in Silico Chemogenomics Approaches To Prioritize Cell-Based HTS Data

Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results.