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Rheumatoid arthritis (RA) is a systemic disease characterized by autoimmunity, joint inflammation, and cartilage destruction, which affects 0.5-1% of the population. Many compounds from herbal medicines show the potentials to treat RA. On this basis, the compounds with good pharmacokinetic behaviors and drug-likeness properties will be further studied and developed. Therefore, the herbal compounds with anti-RA activities were reviewed in this paper, and the cheminformatics tools were used to predict their drug-likeness properties and pharmacokinetic parameters. A total of 90 herbal compounds were analyzed, which were reported to be effective on RA models through anti-inflammation, chondroprotection, immunoregulation, antiangiogenesis, and antioxidation. Most of the herbal compounds have good drug-likeness properties. Most of the compounds can be an alternative and valuable source for anti-RA drug discovery.

Acetylcholinesterase inhibitors (AChEIs) are used for the treatment of Alzheimer's disease (AD). The increase in ACh levels ameliorates the symptoms of the disease. Tacrine is the first clinically approved drug as AChEI used in the treatment of AD. In this paper, we synthesized new tacrine analogs to act on catalytic and peripheral sites of AChE. Their inhibitory activity was evaluated. All novel compounds except 7a showed promising results toward AChE. Two compounds, 10b and 11b, are more potent than tacrine. Furthermore, molecular-modeling studies were performed for these two compounds to rationalize the obtained pharmacological activity. Moreover, various drug-likeness properties of the new compounds were predicted.

Several experiments have been performed to test DNA-binding drugs to cure Leishmania infection. However, there are no details of pharmacoinformatics study. Herein, we have selected a good number of compounds from experimentally verified studies and performed a comparative analysis based on pharmacoinformatics techniques. In silico docking study was performed to observe the molecular level interactions of these known ligands with the DNA receptor by automated computational docking using Glide. A comparison between the calculated interaction energies and in silico ADME/T study was made. In agreement with drug likeness rules, our study suggests that seco-hydroxy-aza-CBI-TMI (compound 4b; GScore, −12.058) is a potential molecule for targeting the DNA to cure leishmaniasis.

A new chemical series was identified via high-throughput screening as having antiproliferative activity on DU-145 human prostate carcinoma cell line (hit compound potency - 2.9 microM). Medicinal chemistry optimization of two peripheral diversity vectors of the hit molecule, independently, led to SAR generalizations and identification of the 'best' moieties. The latter were merged in a single compound that exhibited an over 100-fold better potency than the hit compound. For the most potent compounds it was confirmed that the observed antiproliferative potency was not associated with the compounds' non-specific cytotoxicity.

A collection of reference molecules for ligand-based library design was compiled from the recent scientific literature. Selected properties of this set of 4,236 drugs and drug candidates were analyzed and compared to previous studies. Lipophilicity and molecular weight distributions revealed a trend toward larger, more lipophilic molecules of newer drug candidates. Using the compound collection as a source of reference data, a method for visualization of molecule distributions in a pharmacophore space by self-organizing maps (SOM) was developed which can be used for virtual library design. Potential applications of this "pharmacophore road map" approach are discussed.

Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.

The concept of drug-likeness, an important characteristic for any compound in a screening library, is nevertheless difficult to pin down. Based on our belief that this concept is implicit within the collective experience of working chemists, we devised a data set to capture an intuitive human understanding of both this characteristic and ease of synthesis, a second key characteristic. Five chemists assigned a pair of scores to each of 3980 diverse compounds, with the component scores of each pair corresponding to drug-likeness and ease of synthesis, respectively. Using this data set, we devised binary classifiers with an artificial neural network and a support vector machine. These models were found to efficiently eliminate compounds that are not drug-like and/or hard-to-synthesize derivatives, demonstrating the suitability of these models for use as compound acquisition filters.