Rheumatoid arthritis (RA) is a chronic, polyarticular and symmetric disease that affects around 2.5 million people in the United States of America (USA). The disease seems to have a special predilection for small proximal joints, although virtually all of the peripheral joints in our body may be involved. Rheumatoid arthritis strikes women usually in childbearing age, 3 times more than men. Its main manifestation is inflammation in the joints, but many extra-articular organ systems can be affected. Due to the chronic systemic nature of the disease, the quality of life of patients decreases significantly. Until now, no single therapeutic agent has been found that is universally effective for rheumatoid arthritis, so it has become a rule to apply a combination drug regimen. Recently, several new agents have been introduced with unique mechanisms of action and have been found to produce different degrees of clinical benefit. Among these agents are folate and purine antagonists, alkylating agents and antipyridamines. The much safer treatment for RA would be herbal drugs. We explored the anti-inflammatory natural products through computer-aided drug design by applying virtual screening and molecular docking techniques. In literature, there are many reports about the role of metals in Ayurveda and herbal medicines. We synthesized the metal nanoparticles from Cannabis sativa by the green synthesis approach. Cannabis sativa is an important plant from the family Cannabaceae and has important anti-inflammatory compounds in it to treat the RA. The main idea of this research is to explore all possible means to treat the RA by using medicinal plants. In the first experiment an extensive literature review was done on medicinal plants and three important medicinal plants (Cannabis sativa, Prunella vulgaris and Withania somnifera) were chosen for further studies. These plants possess antiinflammatory, anti-arthritic and anti-rheumatic properties. Virtual screening was done by using different plants related databases to explore the anti-inflammatory compounds. Later, the 13 chosen compounds were characterized and filtered out from medicinal plant species and analyzed for RA by targeting Tumor necrosis factor-α (TNF-α) through in silico analyses. Ligand-based pharmacophore was generated by using the selected compounds and the pharmacophore was used to screen the natural products libraries and we retrieved twenty unique molecules that displayed utmost binding affinity, least binding energies, and effective drug properties. The docking analyses revealed that Ala-22, Glu-23, Ser-65, Gln-67, Tyr-141, Leu-142, Asp-143, Phe-144 and Ala-145 were critical interacting residues for receptor-ligand interactions. It is proposed that the RA patients should use reported compounds for the prescription of RA by targeting TNF-α. This report is opening new dimensions for designing innovative therapeutic targets to cure RA. In the second experiment, the research was further expanded, and we targeted the TNF-α protein, an important receptor protein involved in the RA pathway. TNF-α is a multifunctional cytokine that acts as a central biological mediator for critical immune functions, including inflammation, infection, and antitumor responses. The biologics used to treat RA causes serious side effects such as triggering an autoimmune anti-antibody response or the weakening of the body’s immune defenses. Consequently, there is a dire need to find alternative small-molecule based therapies for TNF-α inhibition. In literature, the small molecules are reported which can inhibit the TNF-α, indirectly. In this study, we found the direct inhibitors of TNF-α by applying the combined in silico approaches. High throughput structure-based and ligand-based virtual screening methods are applied to identify TNF-α inhibitors from 3 different small molecule databases (~256.000 molecules from Otava drug-like green chemical collection, ~500.000 molecules from Otava Tangible database, ~2.500.000 Enamine small molecule database) and ~240.000 molecules from ZINC natural products libraries. Moreover, therapeutic activity prediction, as well as pharmacokinetic and toxicity profiles are also investigated using the MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information, uses binary QSAR models. Molecular Dynamics (MD) simulations were also performed for selected hits to investigate their detailed structural and dynamical analysis beyond docking studies. As a result, at least one hit from each database was identified as novel TNF-α inhibitors after the comprehensive virtual screening, multiple docking, e- Pharmacophore modeling (structure-based pharmacophore modeling), MD simulations, and MetaCore/MetaDrug analysis. Identified hits show predicted the promising anti-arthritic activity and no toxicity. In the third experiment, a novel method is applied to develop energetically optimized, structure-based pharmacophore models for rapid in silico drug screening. Fragment-based docking results were used in the construction of universal e-pharmacophore model development. As target protein, both homodimer and homotrimer forms of TNF-α were considered. The developed pharmacophore was used to screen the small-molecule library Specs-screening compounds (Specs- SC) which includes more than 200.000 drug-like molecules. In another approach, binary QSAR-based models were used to screen Specs-SC, as well as Specs-natural products (NP) which have around 750 compounds, and a library of drugs registered or approved for use in humans NIH''s NCGC pharmaceutical collection (NPC) which has around 7500 molecules. The MetaCore/MetaDrug platform was used for binary QSAR models for therapeutic activity prediction as well as pharmacokinetic and toxicity profile predictions of screening molecules. This platform is constructed based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information. Molecular docking and molecular dynamics (MD) simulations were performed for the selected hit molecules. In the fourth experiment, green synthesis of gold (Au), silver (Ag) and bimetallic alloy Au–Ag nanoparticles (NPs) from aqueous solutions using Cannabis sativa as reducing and stabilizing agent has been presented. The UV-visible spectroscopy was performed for the confirmation of NPs synthesis. Fourier transform-infrared (FTIR) spectroscopy was utilized to identify the possible biomolecules responsible for the reduction and stabilization of the NPs. The composition of elements in the NPs was confirmed by energy-dispersive X-ray spectroscopy analysis. The size and morphology of the synthesized metallic and bimetallic NPs were investigated using X-ray diffraction and scanning electron microscopy. Biological applicability of biosynthesized NPs was tested against five bacterial strains namely Klebsiella pneumonia, Bacillus subtilis (B. subtilis), Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa (P. aeruginosa) and Leishmania major promastigotes. The results showed considerable antibacterial and anti-leishmanial activity. The Au–Ag bimetallic NPs showed improved antibacterial activity against B. subtilis and P. aeruginosa as compared to Au and Ag alone, while maximum anti-leishmanial activity was observed at 250 μg ml−1 NP concentration. These results suggest that biosynthesized NPs can be used as a potent antibiotic and antileishmanial agents. So, these nanoparticles are safer to use as drug delivery vehicles in RA and may also be effective to reduce inflammation.