Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.Comment: 30 pages, 4 figures, 184 reference

Identification of Novel Fluid Biomarkers for Alzheimer\u27s Disease

Clinicopathological studies suggest that Alzheimer\u27s disease: AD) pathology begins to appear ~10-20 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset and progression would, therefore, be invaluable for patient care and efficient clinical trial design. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid: CSF) using an unbiased proteomics approach: two-dimensional difference gel electrophoresis with liquid chromatography tandem mass spectrometry). From this, we identified 47 proteins that differed in abundance between cognitively normal: Clinical Dementia Rating [CDR] 0) and mildly demented: CDR 1) subjects. To validate these findings, we measured a subset of the identified candidate biomarkers by enzyme linked immunosorbent assay: ELISA); promising candidates in this discovery cohort: N=47) were further evaluated by ELISA in a larger validation CSF cohort: N=292) that contained an additional very mildly demented: CDR 0.5) group. Levels of four novel biomarkers were significantly altered in AD, and Receiver-operating characteristic: ROC) analyses using a stepwise logistic regression model identified optimal panels containing these markers that distinguished CDR 0 from CDR\u3e0: tau, YKL-40, NCAM) and CDR 1 from CDR\u3c1: tau, chromogranin-A, carnosinase-I). Plasma levels of the most promising marker, YKL-40, were also found to be increased in CDR 0.5 and 1 groups and to correlate with CSF levels. Importantly, the CSF YKL-40/Aâ42 ratio predicted risk of developing cognitive impairment: CDR 0 to CDR\u3e0 conversion) as well as the best CSF biomarkers identified to date, tau/Aâ42 and p-tau181/Aâ42. Additionally, YKL-40 immunoreactivity was observed within astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to Aâ deposition. Utilizing an alternative, targeted proteomics approach to identify novel biomarkers, 333 CSF samples were evaluated for levels of 190 analytes using a multiplexed Luminex platform. The mean concentrations of 37 analytes were found to differ between CDR 0 and CDR\u3e0 participants. ROC and statistical machine learning algorithms identified novel biomarker panels that improved upon the ability of the current best biomarkers to discriminate very mildly demented from cognitively normal participants, and identified a novel biomarker, Calbindin, with significant prognostic potential

Differentiation of Alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach

Nous avons utilisé l'imagerie TEP avec les traceurs F18-FDG et AV45 en conjonction avec les méthodes de classification du domaine du "Machine Learning". Les images ont été acquises en mode dynamique, une image toutes les 5 minutes. Les données ont été transformées par Analyse en Composantes Principales et Analyse en Composantes Indépendantes. Les images proviennent de trois sources différentes: la base de données ADNI (Alzheimer's Disease Neuroimaging Initiative) et deux protocoles réalisés au sein du centre TEP de l'hôpital Purpan. Pour évaluer la performance de la classification nous avons eu recours à la méthode de validation croisée LOOCV (Leave One Out Cross Validation). Nous donnons une comparaison entre les deux méthodes de classification les plus utilisées, SVM (Support Vector Machine) et les réseaux de neurones artificiels (ANN). La combinaison donnant le meilleur taux de classification semble être SVM et le traceur AV45. Cependant les confusions les plus importantes sont entre les patients MCI et les sujets normaux. Les patients Alzheimer se distinguent relativement mieux puisqu'ils sont retrouvés souvent à plus de 90%. Nous avons évalué la généralisation de telles méthodes de classification en réalisant l'apprentissage sur un ensemble de données et la classification sur un autre ensemble. Nous avons pu atteindre une spécificité de 100% et une sensibilité supérieure à 81%. La méthode SVM semble avoir une meilleure sensibilité que les réseaux de neurones. L'intérêt d'un tel travail est de pouvoir aider à terme au diagnostic de la maladie d'Alzheimer.We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease